{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX F.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 09:55:26
{txt}
{com}. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** APPENDIX F STATISTICAL ANALYSES: REPLICATE MANUSCRIPT MODELS -- SPLIT INTO SINGLE TERM PRESIDENTS [CARTER & BUSH41] VERSUS TWO-TERM PRESIDENTS [REAGAN, CLINTON, & BUSH 43] ****
. **** SINGLE TERM PRESIDENTS [N = 246, 28.61% OF FULL SAMPLE]; TWO TERM PRESIDENTS [N = 614, 71.39% OF FULL SAMPLE] ****
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. 
. 
. 
. 
. *** DESCRIPTIVE STATISTICS OF SPLIT PRESIDENTIAL SUBSAMPLES BY NUMBER OF TERMS]: EMPLOY IN CALCULATING MARGINAL EFFECTS FROM REGRESSION MODELS BELOW ***
. 
. sum zloyalmedian if carter==1 | bush41==1, detail

                        {txt}zloyalmedian
{hline 61}
      Percentiles      Smallest
 1%    {res} -1.63724      -1.717032
{txt} 5%    {res}-1.435101      -1.697527
{txt}10%    {res}-.8003148       -1.63724       {txt}Obs         {res}        246
{txt}25%    {res}-.4385928      -1.624828       {txt}Sum of Wgt. {res}        246

{txt}50%    {res}-.2284749                      {txt}Mean          {res} .0535636
                        {txt}Largest       Std. Dev.     {res} .9336124
{txt}75%    {res} .4018788       2.169706
{txt}90%    {res} 1.753017       2.173253       {txt}Variance      {res} .8716321
{txt}95%    {res} 1.987072       2.299146       {txt}Skewness      {res} .7675933
{txt}99%    {res} 2.173253       2.331063       {txt}Kurtosis      {res} 2.996415
{txt}
{com}. 
. sum zloyalmedian if reagan==1 | clinton==1 | bush43==1, detail

                        {txt}zloyalmedian
{hline 61}
      Percentiles      Smallest
 1%    {res} -1.73299      -1.844698
{txt} 5%    {res}-1.183314      -1.825194
{txt}10%    {res}-.6247732      -1.816328       {txt}Obs         {res}        614
{txt}25%    {res}-.3783058      -1.811008       {txt}Sum of Wgt. {res}        614

{txt}50%    {res}-.1265189                      {txt}Mean          {res} .1584812
                        {txt}Largest       Std. Dev.     {res} .9009449
{txt}75%    {res} .9816979       2.329289
{txt}90%    {res} 1.690957       2.409081       {txt}Variance      {res} .8117017
{txt}95%    {res} 1.925012       2.508377       {txt}Skewness      {res} .6009761
{txt}99%    {res} 2.185664       2.731794       {txt}Kurtosis      {res}  2.86202
{txt}
{com}. 
. 
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. *
. 
. 
. 
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. *
. *
. *
. 
. ** ESTIMATE COX SEMIPARAMETRIC AND WEIBULL PARAMETRIC MODELS PRESENTED IN MANUSCRIPT [MODELS G1A - G4B] ** 
. 
. ** NOTE COVARIATES THAT VARY TRHOUGH TIME ARE BASED ON THE STARTING DATE OF APPOINTED SERVICE [I.E., "OKSTART....""]
. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** MANUSCRIPT-BASED SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS ****
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** APPENDIX F REGRESSION MODELS  ***
. 
. 
. 
. **** MODEL F1A: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr if carter==1 | bush41==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-1033.5194
{txt}Iteration 1:   log pseudolikelihood = {res}-935.75983
{txt}Iteration 2:   log pseudolikelihood = {res}-905.31367
{txt}Iteration 3:   log pseudolikelihood = {res}-902.97238
{txt}Iteration 4:   log pseudolikelihood = {res}-902.90231
{txt}Iteration 5:   log pseudolikelihood = {res} -902.9022
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res} -902.9022

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
                                                {txt}Wald chi2({res}18{txt})    =  {res}    509.97
{txt}Log pseudolikelihood =   {res} -902.9022             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.274496{col 48}{space 2} .1947481{col 59}{space 1}    1.59{col 68}{space 3}0.112{col 76}{space 4} .9446512{col 89}{space 3} 1.719513
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.495091{col 48}{space 2} .3042136{col 59}{space 1}    1.98{col 68}{space 3}0.048{col 76}{space 4}  1.00339{col 89}{space 3} 2.227747
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .7765201{col 48}{space 2} .1274088{col 59}{space 1}   -1.54{col 68}{space 3}0.123{col 76}{space 4} .5629767{col 89}{space 3} 1.071063
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9046779{col 48}{space 2} .1050552{col 59}{space 1}   -0.86{col 68}{space 3}0.388{col 76}{space 4} .7205244{col 89}{space 3} 1.135898
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.032323{col 48}{space 2}  .116712{col 59}{space 1}    0.28{col 68}{space 3}0.778{col 76}{space 4} .8271435{col 89}{space 3} 1.288399
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5213116{col 48}{space 2} .0511988{col 59}{space 1}   -6.63{col 68}{space 3}0.000{col 76}{space 4} .4300309{col 89}{space 3}  .631968
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.118795{col 48}{space 2} .1837226{col 59}{space 1}    0.68{col 68}{space 3}0.494{col 76}{space 4} .8109068{col 89}{space 3} 1.543585
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .9205338{col 48}{space 2} .1811053{col 59}{space 1}   -0.42{col 68}{space 3}0.674{col 76}{space 4} .6260007{col 89}{space 3} 1.353645
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 2.232808{col 48}{space 2} .5596853{col 59}{space 1}    3.20{col 68}{space 3}0.001{col 76}{space 4}  1.36611{col 89}{space 3} 3.649364
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .7236265{col 48}{space 2}  .166388{col 59}{space 1}   -1.41{col 68}{space 3}0.159{col 76}{space 4} .4610969{col 89}{space 3} 1.135629
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 5.8e-145{col 48}{space 2} 5.8e-143{col 59}{space 1}   -3.31{col 68}{space 3}0.001{col 76}{space 4} 1.9e-230{col 89}{space 3} 1.75e-59
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 5.32e+11{col 48}{space 2} 4.49e+12{col 59}{space 1}    3.20{col 68}{space 3}0.001{col 76}{space 4} 35052.88{col 89}{space 3} 8.09e+18
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2}  .060243{col 48}{space 2} .0248623{col 59}{space 1}   -6.81{col 68}{space 3}0.000{col 76}{space 4} .0268297{col 89}{space 3}  .135269
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9947912{col 48}{space 2} .0095296{col 59}{space 1}   -0.55{col 68}{space 3}0.586{col 76}{space 4} .9762878{col 89}{space 3} 1.013645
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2}  1.95245{col 48}{space 2} .4759243{col 59}{space 1}    2.74{col 68}{space 3}0.006{col 76}{space 4} 1.210858{col 89}{space 3} 3.148232
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 3.734976{col 48}{space 2} .8324549{col 59}{space 1}    5.91{col 68}{space 3}0.000{col 76}{space 4} 2.413077{col 89}{space 3} 5.781019
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 9.675945{col 48}{space 2} 2.595313{col 59}{space 1}    8.46{col 68}{space 3}0.000{col 76}{space 4} 5.719807{col 89}{space 3} 16.36837
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 27.05098{col 48}{space 2}  12.4595{col 59}{space 1}    7.16{col 68}{space 3}0.000{col 76}{space 4} 10.96795{col 89}{space 3} 66.71765
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       246{col 28}-1033.519{col 39}-902.9022{col 50}    18{col 58} 1841.804{col 69}   1904.9
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF1A
{txt}
{com}. estout modelF1A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF1A   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.274   {txt}
            {res}      (0.195)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.495*  {txt}
            {res}      (0.304)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.777   {txt}
            {res}      (0.127)   {txt}
{txt}zpecompmed~n{res}        0.905   {txt}
            {res}      (0.105)   {txt}
{txt}zmecompmed~n{res}        1.032   {txt}
            {res}      (0.117)   {txt}
{txt}toplevel2   {res}        0.521***{txt}
            {res}      (0.051)   {txt}
{txt}presagency~n{res}        1.119   {txt}
            {res}      (0.184)   {txt}
{txt}presagency~d{res}        0.921   {txt}
            {res}      (0.181)   {txt}
{txt}subagencyd~n{res}        2.233** {txt}
            {res}      (0.560)   {txt}
{txt}standalone~n{res}        0.724   {txt}
            {res}      (0.166)   {txt}
{txt}okstartsen~n{res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}    5.324e+11** {txt}
            {res}  (4.492e+12)   {txt}
{txt}okcrossover {res}        0.060***{txt}
            {res}      (0.025)   {txt}
{txt}okstartpre~p{res}        0.995   {txt}
            {res}      (0.010)   {txt}
{txt}okstartune~t{res}        1.952** {txt}
            {res}      (0.476)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        3.735***{txt}
            {res}      (0.832)   {txt}
{txt}3.okstarta~r{res}        9.676***{txt}
            {res}      (2.595)   {txt}
{txt}4.okstarta~r{res}       27.051***{txt}
            {res}     (12.459)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1A: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 0.8404716 [0.4018788 - (-0.4385928)] ***
. 
.  
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE F1] **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.8404716, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.8404716

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8084934{col 26}{space 2} .1114926{col 37}{space 1}   -1.54{col 46}{space 3}0.123{col 54}{space 4}  .617013{col 67}{space 3} 1.059397
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF1Azloyal = r(table)
{txt}
{com}. mat list modelF1Azloyal
{res}
{txt}modelF1Azloyal[9,1]
               (1)
     b {res}  .80849336
{txt}    se {res}  .11149264
{txt}     z {res} -1.5415528
{txt}pvalue {res}  .12318231
{txt}    ll {res}  .61701296
{txt}    ul {res}  1.0593967
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL G1B: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  if reagan==1 | clinton==1 | bush43==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:   log pseudolikelihood = {res}-3286.3319
{txt}Iteration 1:   log pseudolikelihood = {res}-3120.4375
{txt}Iteration 2:   log pseudolikelihood = {res}-3096.6379
{txt}Iteration 3:   log pseudolikelihood = {res}-3096.1859
{txt}Iteration 4:   log pseudolikelihood = {res}-3096.1852
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-3096.1852

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
                                                {txt}Wald chi2({res}22{txt})    =  {res}    888.75
{txt}Log pseudolikelihood =   {res}-3096.1852             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.451163{col 48}{space 2} .1669569{col 59}{space 1}    3.24{col 68}{space 3}0.001{col 76}{space 4} 1.158204{col 89}{space 3} 1.818224
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.140578{col 48}{space 2} .1535751{col 59}{space 1}    0.98{col 68}{space 3}0.329{col 76}{space 4} .8760192{col 89}{space 3} 1.485034
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6103056{col 48}{space 2} .0799223{col 59}{space 1}   -3.77{col 68}{space 3}0.000{col 76}{space 4} .4721485{col 89}{space 3} .7888894
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9962355{col 48}{space 2} .0772842{col 59}{space 1}   -0.05{col 68}{space 3}0.961{col 76}{space 4} .8557147{col 89}{space 3} 1.159832
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2}   1.0173{col 48}{space 2} .0671522{col 59}{space 1}    0.26{col 68}{space 3}0.795{col 76}{space 4} .8938421{col 89}{space 3} 1.157809
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2}  .560115{col 48}{space 2} .0546426{col 59}{space 1}   -5.94{col 68}{space 3}0.000{col 76}{space 4} .4626338{col 89}{space 3} .6781364
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.530668{col 48}{space 2} .1495897{col 59}{space 1}    4.36{col 68}{space 3}0.000{col 76}{space 4} 1.263847{col 89}{space 3}  1.85382
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.445435{col 48}{space 2} .1641183{col 59}{space 1}    3.24{col 68}{space 3}0.001{col 76}{space 4} 1.157047{col 89}{space 3} 1.805702
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} .9966395{col 48}{space 2} .1633471{col 59}{space 1}   -0.02{col 68}{space 3}0.984{col 76}{space 4} .7228164{col 89}{space 3} 1.374194
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .8102399{col 48}{space 2} .1093538{col 59}{space 1}   -1.56{col 68}{space 3}0.119{col 76}{space 4} .6219156{col 89}{space 3} 1.055591
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0005744{col 48}{space 2} .0022957{col 59}{space 1}   -1.87{col 68}{space 3}0.062{col 76}{space 4} 2.27e-07{col 89}{space 3} 1.450346
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 3.414566{col 48}{space 2}  1.45214{col 59}{space 1}    2.89{col 68}{space 3}0.004{col 76}{space 4} 1.483673{col 89}{space 3} 7.858377
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2414703{col 48}{space 2} .0392826{col 59}{space 1}   -8.73{col 68}{space 3}0.000{col 76}{space 4} .1755455{col 89}{space 3} .3321527
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.000213{col 48}{space 2} .0041597{col 59}{space 1}    0.05{col 68}{space 3}0.959{col 76}{space 4} .9920932{col 89}{space 3} 1.008399
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9033887{col 48}{space 2} .0717307{col 59}{space 1}   -1.28{col 68}{space 3}0.201{col 76}{space 4} .7731926{col 89}{space 3} 1.055508
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.846138{col 48}{space 2} .5144736{col 59}{space 1}    2.20{col 68}{space 3}0.028{col 76}{space 4} 1.069191{col 89}{space 3}  3.18767
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.194346{col 48}{space 2} .9298685{col 59}{space 1}    6.47{col 68}{space 3}0.000{col 76}{space 4} 2.716167{col 89}{space 3} 6.476971
{txt}{space 32}4  {c |}{col 36}{res}{space 2}  2.03382{col 48}{space 2} .6462332{col 59}{space 1}    2.23{col 68}{space 3}0.025{col 76}{space 4} 1.091061{col 89}{space 3} 3.791197
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.212055{col 48}{space 2} .1797392{col 59}{space 1}    1.30{col 68}{space 3}0.195{col 76}{space 4} .9063481{col 89}{space 3} 1.620875
{txt}{space 32}6  {c |}{col 36}{res}{space 2}  2.43227{col 48}{space 2} .3588925{col 59}{space 1}    6.02{col 68}{space 3}0.000{col 76}{space 4} 1.821433{col 89}{space 3} 3.247958
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 4.446406{col 48}{space 2} 1.026513{col 59}{space 1}    6.46{col 68}{space 3}0.000{col 76}{space 4} 2.828119{col 89}{space 3} 6.990698
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 5.879296{col 48}{space 2} 1.818817{col 59}{space 1}    5.73{col 68}{space 3}0.000{col 76}{space 4} 3.206254{col 89}{space 3} 10.78084
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       614{col 28}-3286.332{col 39}-3096.185{col 50}    22{col 58}  6236.37{col 69}  6333.61
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF1B
{txt}
{com}. estout modelF1B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF1B   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.451** {txt}
            {res}      (0.167)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.141   {txt}
            {res}      (0.154)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.610***{txt}
            {res}      (0.080)   {txt}
{txt}zpecompmed~n{res}        0.996   {txt}
            {res}      (0.077)   {txt}
{txt}zmecompmed~n{res}        1.017   {txt}
            {res}      (0.067)   {txt}
{txt}toplevel2   {res}        0.560***{txt}
            {res}      (0.055)   {txt}
{txt}presagency~n{res}        1.531***{txt}
            {res}      (0.150)   {txt}
{txt}presagency~d{res}        1.445** {txt}
            {res}      (0.164)   {txt}
{txt}subagencyd~n{res}        0.997   {txt}
            {res}      (0.163)   {txt}
{txt}standalone~n{res}        0.810   {txt}
            {res}      (0.109)   {txt}
{txt}okstartsen~n{res}        0.001   {txt}
            {res}      (0.002)   {txt}
{txt}okstartfil~e{res}        3.415** {txt}
            {res}      (1.452)   {txt}
{txt}okcrossover {res}        0.241***{txt}
            {res}      (0.039)   {txt}
{txt}okstartpre~p{res}        1.000   {txt}
            {res}      (0.004)   {txt}
{txt}okstartune~t{res}        0.903   {txt}
            {res}      (0.072)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.846*  {txt}
            {res}      (0.514)   {txt}
{txt}3.okstarta~r{res}        4.194***{txt}
            {res}      (0.930)   {txt}
{txt}4.okstarta~r{res}        2.034*  {txt}
            {res}      (0.646)   {txt}
{txt}5.okstarta~r{res}        1.212   {txt}
            {res}      (0.180)   {txt}
{txt}6.okstarta~r{res}        2.432***{txt}
            {res}      (0.359)   {txt}
{txt}7.okstarta~r{res}        4.446***{txt}
            {res}      (1.027)   {txt}
{txt}8.okstarta~r{res}        5.879***{txt}
            {res}      (1.819)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  1.3600037  [0.9816979 - (-0.3783058)] ***
. 
. 
. 
. ** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE F1] **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3600037, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3600037

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5109091{col 26}{space 2} .0909922{col 37}{space 1}   -3.77{col 46}{space 3}0.000{col 54}{space 4} .3603676{col 67}{space 3} .7243384
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF1Bzloyal = r(table)
{txt}
{com}. mat list modelF1Bzloyal
{res}
{txt}modelF1Bzloyal[9,1]
               (1)
     b {res}   .5109091
{txt}    se {res}  .09099224
{txt}     z {res} -3.7707385
{txt}pvalue {res}  .00016277
{txt}    ll {res}  .36036761
{txt}    ul {res}  .72433841
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. *
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL F2A: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency bush41 if carter==1 | bush41==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
note: bush41 omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-1033.5194
{txt}Iteration 1:   log pseudolikelihood = {res}-905.94986
{txt}Iteration 2:   log pseudolikelihood = {res}-874.96548
{txt}Iteration 3:   log pseudolikelihood = {res} -872.7407
{txt}Iteration 4:   log pseudolikelihood = {res}-872.72069
{txt}Iteration 5:   log pseudolikelihood = {res}-872.72068
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-872.72068

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
                                                {txt}Wald chi2({res}38{txt})    =  {res}   6138.35
{txt}Log pseudolikelihood =   {res}-872.72068             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.382819{col 48}{space 2} .2408103{col 59}{space 1}    1.86{col 68}{space 3}0.063{col 76}{space 4}  .982954{col 89}{space 3} 1.945348
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.416688{col 48}{space 2}  .419712{col 59}{space 1}    1.18{col 68}{space 3}0.240{col 76}{space 4} .7926747{col 89}{space 3} 2.531941
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6530781{col 48}{space 2} .1316383{col 59}{space 1}   -2.11{col 68}{space 3}0.035{col 76}{space 4} .4399385{col 89}{space 3} .9694789
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9573451{col 48}{space 2} .1440311{col 59}{space 1}   -0.29{col 68}{space 3}0.772{col 76}{space 4} .7128638{col 89}{space 3} 1.285673
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .8994857{col 48}{space 2} .1184671{col 59}{space 1}   -0.80{col 68}{space 3}0.421{col 76}{space 4} .6948426{col 89}{space 3}   1.1644
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .3969576{col 48}{space 2}  .054622{col 59}{space 1}   -6.71{col 68}{space 3}0.000{col 76}{space 4}  .303122{col 89}{space 3} .5198413
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2}  1.03927{col 48}{space 2} .3540809{col 59}{space 1}    0.11{col 68}{space 3}0.910{col 76}{space 4} .5329946{col 89}{space 3} 2.026442
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .9257439{col 48}{space 2} .3132123{col 59}{space 1}   -0.23{col 68}{space 3}0.820{col 76}{space 4} .4769787{col 89}{space 3}  1.79673
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 6.294409{col 48}{space 2} 1.721763{col 59}{space 1}    6.73{col 68}{space 3}0.000{col 76}{space 4} 3.682295{col 89}{space 3} 10.75948
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 3.345854{col 48}{space 2} 1.381727{col 59}{space 1}    2.92{col 68}{space 3}0.003{col 76}{space 4} 1.489323{col 89}{space 3} 7.516662
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 1.4e-138{col 48}{space 2} 1.9e-136{col 59}{space 1}   -2.28{col 68}{space 3}0.022{col 76}{space 4} 6.5e-257{col 89}{space 3} 2.91e-20
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 2.69e+11{col 48}{space 2} 3.15e+12{col 59}{space 1}    2.24{col 68}{space 3}0.025{col 76}{space 4} 27.07449{col 89}{space 3} 2.66e+21
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .0447102{col 48}{space 2} .0272209{col 59}{space 1}   -5.10{col 68}{space 3}0.000{col 76}{space 4} .0135572{col 89}{space 3} .1474494
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9833781{col 48}{space 2}  .011147{col 59}{space 1}   -1.48{col 68}{space 3}0.139{col 76}{space 4} .9617714{col 89}{space 3}  1.00547
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 2.316755{col 48}{space 2} .7761128{col 59}{space 1}    2.51{col 68}{space 3}0.012{col 76}{space 4} 1.201511{col 89}{space 3} 4.467171
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.856186{col 48}{space 2} .9318909{col 59}{space 1}    3.22{col 68}{space 3}0.001{col 76}{space 4} 1.506829{col 89}{space 3} 5.413884
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 11.20728{col 48}{space 2} 3.565793{col 59}{space 1}    7.60{col 68}{space 3}0.000{col 76}{space 4} 6.007247{col 89}{space 3}  20.9086
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 28.79715{col 48}{space 2} 18.08038{col 59}{space 1}    5.35{col 68}{space 3}0.000{col 76}{space 4} 8.412403{col 89}{space 3} 98.57775
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 7.955593{col 48}{space 2} 2.944457{col 59}{space 1}    5.60{col 68}{space 3}0.000{col 76}{space 4} 3.851528{col 89}{space 3} 16.43282
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 1.369982{col 48}{space 2} .5304872{col 59}{space 1}    0.81{col 68}{space 3}0.416{col 76}{space 4}  .641373{col 89}{space 3}   2.9263
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .3092482{col 48}{space 2}  .117346{col 59}{space 1}   -3.09{col 68}{space 3}0.002{col 76}{space 4} .1469988{col 89}{space 3} .6505798
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .8686564{col 48}{space 2} .2605437{col 59}{space 1}   -0.47{col 68}{space 3}0.639{col 76}{space 4}  .482547{col 89}{space 3}  1.56371
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 1.245428{col 48}{space 2} .4363704{col 59}{space 1}    0.63{col 68}{space 3}0.531{col 76}{space 4} .6267243{col 89}{space 3} 2.474918
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 2.270711{col 48}{space 2} .8453708{col 59}{space 1}    2.20{col 68}{space 3}0.028{col 76}{space 4} 1.094624{col 89}{space 3} 4.710409
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 4.984628{col 48}{space 2} 1.836789{col 59}{space 1}    4.36{col 68}{space 3}0.000{col 76}{space 4} 2.420878{col 89}{space 3} 10.26343
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.783373{col 48}{space 2} .9598888{col 59}{space 1}    2.97{col 68}{space 3}0.003{col 76}{space 4} 1.415864{col 89}{space 3} 5.471688
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 2.670973{col 48}{space 2} .8443658{col 59}{space 1}    3.11{col 68}{space 3}0.002{col 76}{space 4} 1.437414{col 89}{space 3} 4.963145
{txt}{space 31}13  {c |}{col 36}{res}{space 2} .6548566{col 48}{space 2} .2235836{col 59}{space 1}   -1.24{col 68}{space 3}0.215{col 76}{space 4} .3353712{col 89}{space 3} 1.278694
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 4.957537{col 48}{space 2} 1.747639{col 59}{space 1}    4.54{col 68}{space 3}0.000{col 76}{space 4} 2.484271{col 89}{space 3} 9.893112
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.017871{col 48}{space 2} .3291707{col 59}{space 1}    0.05{col 68}{space 3}0.956{col 76}{space 4} .5400347{col 89}{space 3}  1.91851
{txt}{space 31}16  {c |}{col 36}{res}{space 2} 2.400585{col 48}{space 2} .5334908{col 59}{space 1}    3.94{col 68}{space 3}0.000{col 76}{space 4} 1.552928{col 89}{space 3} 3.710929
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 3.840664{col 48}{space 2} .9968453{col 59}{space 1}    5.18{col 68}{space 3}0.000{col 76}{space 4} 2.309281{col 89}{space 3} 6.387574
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 1.904597{col 48}{space 2} .6975715{col 59}{space 1}    1.76{col 68}{space 3}0.079{col 76}{space 4} .9290618{col 89}{space 3} 3.904464
{txt}{space 31}19  {c |}{col 36}{res}{space 2} 2.381312{col 48}{space 2} .7680527{col 59}{space 1}    2.69{col 68}{space 3}0.007{col 76}{space 4} 1.265538{col 89}{space 3} 4.480817
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .3256418{col 48}{space 2} .1309985{col 59}{space 1}   -2.79{col 68}{space 3}0.005{col 76}{space 4}   .14802{col 89}{space 3} .7164069
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .2104761{col 48}{space 2} .0632346{col 59}{space 1}   -5.19{col 68}{space 3}0.000{col 76}{space 4} .1168076{col 89}{space 3} .3792576
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .9109809{col 48}{space 2} .4091176{col 59}{space 1}   -0.21{col 68}{space 3}0.836{col 76}{space 4} .3777794{col 89}{space 3} 2.196748
{txt}{space 31}23  {c |}{col 36}{res}{space 2} 1.277699{col 48}{space 2} .5565097{col 59}{space 1}    0.56{col 68}{space 3}0.574{col 76}{space 4} .5441046{col 89}{space 3} 3.000369
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .1285362{col 48}{space 2}  .092735{col 59}{space 1}   -2.84{col 68}{space 3}0.004{col 76}{space 4} .0312542{col 89}{space 3}  .528618
{txt}{space 31}25  {c |}{col 36}{res}{space 2} .7511948{col 48}{space 2} .2218558{col 59}{space 1}   -0.97{col 68}{space 3}0.333{col 76}{space 4} .4210765{col 89}{space 3} 1.340121
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .8908426{col 48}{space 2} .3100431{col 59}{space 1}   -0.33{col 68}{space 3}0.740{col 76}{space 4} .4503543{col 89}{space 3} 1.762169
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 4.231262{col 48}{space 2} 1.524749{col 59}{space 1}    4.00{col 68}{space 3}0.000{col 76}{space 4}  2.08803{col 89}{space 3} 8.574387
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 1.736743{col 48}{space 2} .7501699{col 59}{space 1}    1.28{col 68}{space 3}0.201{col 76}{space 4} .7448469{col 89}{space 3} 4.049525
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 2.045728{col 48}{space 2} 1.002427{col 59}{space 1}    1.46{col 68}{space 3}0.144{col 76}{space 4} .7829807{col 89}{space 3} 5.344966
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 1.138583{col 48}{space 2} .2544534{col 59}{space 1}    0.58{col 68}{space 3}0.561{col 76}{space 4}  .734744{col 89}{space 3} 1.764385
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.760494{col 48}{space 2} 1.429658{col 59}{space 1}    3.48{col 68}{space 3}0.000{col 76}{space 4} 1.784993{col 89}{space 3} 7.922334
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.826785{col 48}{space 2} 1.133935{col 59}{space 1}    0.97{col 68}{space 3}0.332{col 76}{space 4} .5411574{col 89}{space 3} 6.166679
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.341435{col 48}{space 2}  .141581{col 59}{space 1}    2.78{col 68}{space 3}0.005{col 76}{space 4} 1.090762{col 89}{space 3} 1.649716
{txt}{space 31}54  {c |}{col 36}{res}{space 2} .7093087{col 48}{space 2} .3781863{col 59}{space 1}   -0.64{col 68}{space 3}0.519{col 76}{space 4} .2494567{col 89}{space 3} 2.016858
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .7402137{col 48}{space 2} .3467143{col 59}{space 1}   -0.64{col 68}{space 3}0.521{col 76}{space 4} .2955673{col 89}{space 3} 1.853779
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 2.079564{col 48}{space 2} 1.478445{col 59}{space 1}    1.03{col 68}{space 3}0.303{col 76}{space 4} .5162018{col 89}{space 3} 8.377709
{txt}{space 31}59  {c |}{col 36}{res}{space 2} 1.147442{col 48}{space 2} .8421125{col 59}{space 1}    0.19{col 68}{space 3}0.851{col 76}{space 4} .2722891{col 89}{space 3} 4.835386
{txt}{space 31}60  {c |}{col 36}{res}{space 2}  .532176{col 48}{space 2} .1959617{col 59}{space 1}   -1.71{col 68}{space 3}0.087{col 76}{space 4} .2585948{col 89}{space 3} 1.095193
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}bush41 {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       246{col 28}-1033.519{col 39}-872.7207{col 50}    38{col 58} 1821.441{col 69} 1954.644
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF2A
{txt}
{com}. estout modelF2A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF2A   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.383   {txt}
            {res}      (0.241)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.417   {txt}
            {res}      (0.420)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.653*  {txt}
            {res}      (0.132)   {txt}
{txt}zpecompmed~n{res}        0.957   {txt}
            {res}      (0.144)   {txt}
{txt}zmecompmed~n{res}        0.899   {txt}
            {res}      (0.118)   {txt}
{txt}toplevel2   {res}        0.397***{txt}
            {res}      (0.055)   {txt}
{txt}presagency~n{res}        1.039   {txt}
            {res}      (0.354)   {txt}
{txt}presagency~d{res}        0.926   {txt}
            {res}      (0.313)   {txt}
{txt}subagencyd~n{res}        6.294***{txt}
            {res}      (1.722)   {txt}
{txt}standalone~n{res}        3.346** {txt}
            {res}      (1.382)   {txt}
{txt}okstartsen~n{res}        0.000*  {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}    2.685e+11*  {txt}
            {res}  (3.154e+12)   {txt}
{txt}okcrossover {res}        0.045***{txt}
            {res}      (0.027)   {txt}
{txt}okstartpre~p{res}        0.983   {txt}
            {res}      (0.011)   {txt}
{txt}okstartune~t{res}        2.317*  {txt}
            {res}      (0.776)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        2.856** {txt}
            {res}      (0.932)   {txt}
{txt}3.okstarta~r{res}       11.207***{txt}
            {res}      (3.566)   {txt}
{txt}4.okstarta~r{res}       28.797***{txt}
            {res}     (18.080)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        7.956***{txt}
            {res}      (2.944)   {txt}
{txt}3.sbagency  {res}        1.370   {txt}
            {res}      (0.530)   {txt}
{txt}4.sbagency  {res}        0.309** {txt}
            {res}      (0.117)   {txt}
{txt}5.sbagency  {res}        0.869   {txt}
            {res}      (0.261)   {txt}
{txt}6.sbagency  {res}        1.245   {txt}
            {res}      (0.436)   {txt}
{txt}7.sbagency  {res}        2.271*  {txt}
            {res}      (0.845)   {txt}
{txt}8.sbagency  {res}        4.985***{txt}
            {res}      (1.837)   {txt}
{txt}9.sbagency  {res}        2.783** {txt}
            {res}      (0.960)   {txt}
{txt}12.sbagency {res}        2.671** {txt}
            {res}      (0.844)   {txt}
{txt}13.sbagency {res}        0.655   {txt}
            {res}      (0.224)   {txt}
{txt}14.sbagency {res}        4.958***{txt}
            {res}      (1.748)   {txt}
{txt}15.sbagency {res}        1.018   {txt}
            {res}      (0.329)   {txt}
{txt}16.sbagency {res}        2.401***{txt}
            {res}      (0.533)   {txt}
{txt}17.sbagency {res}        3.841***{txt}
            {res}      (0.997)   {txt}
{txt}18.sbagency {res}        1.905   {txt}
            {res}      (0.698)   {txt}
{txt}19.sbagency {res}        2.381** {txt}
            {res}      (0.768)   {txt}
{txt}20.sbagency {res}        0.326** {txt}
            {res}      (0.131)   {txt}
{txt}21.sbagency {res}        0.210***{txt}
            {res}      (0.063)   {txt}
{txt}22.sbagency {res}        0.911   {txt}
            {res}      (0.409)   {txt}
{txt}23.sbagency {res}        1.278   {txt}
            {res}      (0.557)   {txt}
{txt}24.sbagency {res}        0.129** {txt}
            {res}      (0.093)   {txt}
{txt}25.sbagency {res}        0.751   {txt}
            {res}      (0.222)   {txt}
{txt}26.sbagency {res}        0.891   {txt}
            {res}      (0.310)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        4.231***{txt}
            {res}      (1.525)   {txt}
{txt}29.sbagency {res}        1.737   {txt}
            {res}      (0.750)   {txt}
{txt}30.sbagency {res}        2.046   {txt}
            {res}      (1.002)   {txt}
{txt}50.sbagency {res}        1.139   {txt}
            {res}      (0.254)   {txt}
{txt}51.sbagency {res}        3.760***{txt}
            {res}      (1.430)   {txt}
{txt}52.sbagency {res}        1.827   {txt}
            {res}      (1.134)   {txt}
{txt}53.sbagency {res}        1.341** {txt}
            {res}      (0.142)   {txt}
{txt}54.sbagency {res}        0.709   {txt}
            {res}      (0.378)   {txt}
{txt}56.sbagency {res}        0.740   {txt}
            {res}      (0.347)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        2.080   {txt}
            {res}      (1.478)   {txt}
{txt}59.sbagency {res}        1.147   {txt}
            {res}      (0.842)   {txt}
{txt}60.sbagency {res}        0.532   {txt}
            {res}      (0.196)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}bush41      {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 0.8404716 [0.4018788 - (-0.4385928)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.8404716, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.8404716

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6990101{col 26}{space 2} .1184196{col 37}{space 1}   -2.11{col 46}{space 3}0.035{col 54}{space 4} .5015117{col 67}{space 3} .9742847
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF2Azloyal = r(table)
{txt}
{com}. mat list modelF2Azloyal
{res}
{txt}modelF2Azloyal[9,1]
               (1)
     b {res}  .69901011
{txt}    se {res}  .11841959
{txt}     z {res} -2.1137429
{txt}pvalue {res}  .03453723
{txt}    ll {res}  .50151168
{txt}    ul {res}  .97428466
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. **** MODEL F2B: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency clinton bush43 if reagan==1 | clinton==1 | bush43==1,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-3286.3319
{txt}Iteration 1:   log pseudolikelihood = {res} -3097.173
{txt}Iteration 2:   log pseudolikelihood = {res}-3070.6266
{txt}Iteration 3:   log pseudolikelihood = {res}-3070.1426
{txt}Iteration 4:   log pseudolikelihood = {res}-3070.1412
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-3070.1412

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
                                                {txt}Wald chi2({res}40{txt})    =  {res}  84487.71
{txt}Log pseudolikelihood =   {res}-3070.1412             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.384758{col 48}{space 2} .1930989{col 59}{space 1}    2.33{col 68}{space 3}0.020{col 76}{space 4} 1.053604{col 89}{space 3} 1.819996
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.222201{col 48}{space 2} .2545219{col 59}{space 1}    0.96{col 68}{space 3}0.335{col 76}{space 4} .8126078{col 89}{space 3} 1.838249
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .5855164{col 48}{space 2} .0947957{col 59}{space 1}   -3.31{col 68}{space 3}0.001{col 76}{space 4} .4263131{col 89}{space 3} .8041728
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9807628{col 48}{space 2} .0851181{col 59}{space 1}   -0.22{col 68}{space 3}0.823{col 76}{space 4} .8273519{col 89}{space 3}  1.16262
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.037243{col 48}{space 2} .0706077{col 59}{space 1}    0.54{col 68}{space 3}0.591{col 76}{space 4} .9076892{col 89}{space 3} 1.185288
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .4810571{col 48}{space 2} .0592893{col 59}{space 1}   -5.94{col 68}{space 3}0.000{col 76}{space 4} .3778225{col 89}{space 3} .6124992
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .7210682{col 48}{space 2} .2599522{col 59}{space 1}   -0.91{col 68}{space 3}0.364{col 76}{space 4} .3557212{col 89}{space 3} 1.461648
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .7199251{col 48}{space 2} .2650774{col 59}{space 1}   -0.89{col 68}{space 3}0.372{col 76}{space 4} .3498435{col 89}{space 3} 1.481497
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.479465{col 48}{space 2}  .402395{col 59}{space 1}    1.44{col 68}{space 3}0.150{col 76}{space 4} .8681382{col 89}{space 3} 2.521275
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.494636{col 48}{space 2} .5373045{col 59}{space 1}    1.12{col 68}{space 3}0.264{col 76}{space 4} .7388195{col 89}{space 3} 3.023658
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 1.78e-08{col 48}{space 2} 1.71e-07{col 59}{space 1}   -1.86{col 68}{space 3}0.063{col 76}{space 4} 1.20e-16{col 89}{space 3} 2.640616
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 76.80557{col 48}{space 2} 159.0401{col 59}{space 1}    2.10{col 68}{space 3}0.036{col 76}{space 4} 1.326852{col 89}{space 3} 4445.932
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2164222{col 48}{space 2} .0390716{col 59}{space 1}   -8.48{col 68}{space 3}0.000{col 76}{space 4} .1519256{col 89}{space 3} .3082993
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9979876{col 48}{space 2} .0060239{col 59}{space 1}   -0.33{col 68}{space 3}0.739{col 76}{space 4} .9862504{col 89}{space 3} 1.009864
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.029905{col 48}{space 2} .1056096{col 59}{space 1}    0.29{col 68}{space 3}0.774{col 76}{space 4} .8423881{col 89}{space 3} 1.259163
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.486569{col 48}{space 2} .4938218{col 59}{space 1}    1.19{col 68}{space 3}0.233{col 76}{space 4} .7752204{col 89}{space 3} 2.850656
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 3.416903{col 48}{space 2} 1.113479{col 59}{space 1}    3.77{col 68}{space 3}0.000{col 76}{space 4} 1.804049{col 89}{space 3} 6.471679
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.740375{col 48}{space 2} .6446998{col 59}{space 1}    1.50{col 68}{space 3}0.135{col 76}{space 4} .8420269{col 89}{space 3} 3.597159
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.336453{col 48}{space 2} .3289129{col 59}{space 1}    1.18{col 68}{space 3}0.239{col 76}{space 4} .8250222{col 89}{space 3}  2.16492
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.865046{col 48}{space 2} .6979026{col 59}{space 1}    4.32{col 68}{space 3}0.000{col 76}{space 4} 1.777402{col 89}{space 3} 4.618251
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 5.408076{col 48}{space 2} 1.655623{col 59}{space 1}    5.51{col 68}{space 3}0.000{col 76}{space 4} 2.967952{col 89}{space 3} 9.854367
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 7.716355{col 48}{space 2}  3.26507{col 59}{space 1}    4.83{col 68}{space 3}0.000{col 76}{space 4}  3.36696{col 89}{space 3} 17.68424
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.320007{col 48}{space 2} .8474508{col 59}{space 1}    2.30{col 68}{space 3}0.021{col 76}{space 4} 1.133869{col 89}{space 3} 4.746961
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.169697{col 48}{space 2} .7696218{col 59}{space 1}    2.18{col 68}{space 3}0.029{col 76}{space 4} 1.082595{col 89}{space 3} 4.348427
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.415159{col 48}{space 2} .3987002{col 59}{space 1}    1.23{col 68}{space 3}0.218{col 76}{space 4} .8146889{col 89}{space 3} 2.458209
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.131766{col 48}{space 2} .3362805{col 59}{space 1}    0.42{col 68}{space 3}0.677{col 76}{space 4} .6321786{col 89}{space 3} 2.026158
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.141002{col 48}{space 2} .7450197{col 59}{space 1}    4.83{col 68}{space 3}0.000{col 76}{space 4} 1.973196{col 89}{space 3} 4.999956
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.481606{col 48}{space 2} .5673316{col 59}{space 1}    1.03{col 68}{space 3}0.305{col 76}{space 4} .6995097{col 89}{space 3} 3.138138
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.141916{col 48}{space 2} .7382591{col 59}{space 1}    2.21{col 68}{space 3}0.027{col 76}{space 4} 1.089976{col 89}{space 3} 4.209088
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 1.910149{col 48}{space 2} .6028093{col 59}{space 1}    2.05{col 68}{space 3}0.040{col 76}{space 4} 1.029065{col 89}{space 3} 3.545614
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 3.957267{col 48}{space 2} 1.549213{col 59}{space 1}    3.51{col 68}{space 3}0.000{col 76}{space 4} 1.837225{col 89}{space 3} 8.523705
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.789565{col 48}{space 2} .5048559{col 59}{space 1}    2.06{col 68}{space 3}0.039{col 76}{space 4} 1.029471{col 89}{space 3} 3.110862
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.852989{col 48}{space 2} .6034402{col 59}{space 1}    1.89{col 68}{space 3}0.058{col 76}{space 4} .9787514{col 89}{space 3}  3.50811
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.169018{col 48}{space 2} .7733732{col 59}{space 1}    2.17{col 68}{space 3}0.030{col 76}{space 4} 1.078359{col 89}{space 3} 4.362776
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.584018{col 48}{space 2} .5857602{col 59}{space 1}    1.24{col 68}{space 3}0.214{col 76}{space 4} .7673457{col 89}{space 3} 3.269861
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .7673102{col 48}{space 2} .1584841{col 59}{space 1}   -1.28{col 68}{space 3}0.200{col 76}{space 4} .5118698{col 89}{space 3} 1.150224
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.244275{col 48}{space 2} .1119089{col 59}{space 1}    2.43{col 68}{space 3}0.015{col 76}{space 4} 1.043182{col 89}{space 3} 1.484132
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.030147{col 48}{space 2} .7736837{col 59}{space 1}    1.86{col 68}{space 3}0.063{col 76}{space 4} .9619156{col 89}{space 3} 4.284678
{txt}{space 31}19  {c |}{col 36}{res}{space 2}  .855285{col 48}{space 2} .1460815{col 59}{space 1}   -0.92{col 68}{space 3}0.360{col 76}{space 4} .6119654{col 89}{space 3} 1.195349
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2869704{col 48}{space 2} .1093212{col 59}{space 1}   -3.28{col 68}{space 3}0.001{col 76}{space 4} .1360105{col 89}{space 3} .6054827
{txt}{space 31}21  {c |}{col 36}{res}{space 2} 1.330327{col 48}{space 2} .2009205{col 59}{space 1}    1.89{col 68}{space 3}0.059{col 76}{space 4} .9894654{col 89}{space 3} 1.788612
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .5261936{col 48}{space 2} .2358495{col 59}{space 1}   -1.43{col 68}{space 3}0.152{col 76}{space 4} .2185856{col 89}{space 3} 1.266688
{txt}{space 31}23  {c |}{col 36}{res}{space 2}  1.41367{col 48}{space 2} .5014115{col 59}{space 1}    0.98{col 68}{space 3}0.329{col 76}{space 4} .7054031{col 89}{space 3}  2.83308
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .5485531{col 48}{space 2} .2245642{col 59}{space 1}   -1.47{col 68}{space 3}0.142{col 76}{space 4} .2458995{col 89}{space 3} 1.223714
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.864355{col 48}{space 2} .2731594{col 59}{space 1}    4.25{col 68}{space 3}0.000{col 76}{space 4} 1.398986{col 89}{space 3} 2.484529
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .6376298{col 48}{space 2} .1140248{col 59}{space 1}   -2.52{col 68}{space 3}0.012{col 76}{space 4} .4491089{col 89}{space 3} .9052855
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2}  1.05084{col 48}{space 2} .1743439{col 59}{space 1}    0.30{col 68}{space 3}0.765{col 76}{space 4} .7591274{col 89}{space 3} 1.454651
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 5.354665{col 48}{space 2} 2.199574{col 59}{space 1}    4.08{col 68}{space 3}0.000{col 76}{space 4} 2.393746{col 89}{space 3} 11.97806
{txt}{space 31}30  {c |}{col 36}{res}{space 2}  1.55217{col 48}{space 2} .4940547{col 59}{space 1}    1.38{col 68}{space 3}0.167{col 76}{space 4} .8317687{col 89}{space 3} 2.896518
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.295322{col 48}{space 2} .5413184{col 59}{space 1}    3.52{col 68}{space 3}0.000{col 76}{space 4} 1.445773{col 89}{space 3} 3.644074
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.862871{col 48}{space 2} .7974892{col 59}{space 1}    3.78{col 68}{space 3}0.000{col 76}{space 4} 1.658398{col 89}{space 3} 4.942138
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.602099{col 48}{space 2} .6002939{col 59}{space 1}    1.26{col 68}{space 3}0.208{col 76}{space 4} .7686888{col 89}{space 3} 3.339088
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.555467{col 48}{space 2} .3820935{col 59}{space 1}    1.80{col 68}{space 3}0.072{col 76}{space 4}  .961096{col 89}{space 3} 2.517414
{txt}{space 31}54  {c |}{col 36}{res}{space 2}  2.37067{col 48}{space 2} .5271313{col 59}{space 1}    3.88{col 68}{space 3}0.000{col 76}{space 4}  1.53321{col 89}{space 3}  3.66556
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.275179{col 48}{space 2} .4837213{col 59}{space 1}    0.64{col 68}{space 3}0.522{col 76}{space 4} .6062894{col 89}{space 3} 2.682023
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .7631383{col 48}{space 2} .3391789{col 59}{space 1}   -0.61{col 68}{space 3}0.543{col 76}{space 4} .3193629{col 89}{space 3} 1.823569
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.339652{col 48}{space 2} .4771619{col 59}{space 1}    0.82{col 68}{space 3}0.412{col 76}{space 4}  .666512{col 89}{space 3} 2.692625
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .4283324{col 48}{space 2} .1555484{col 59}{space 1}   -2.33{col 68}{space 3}0.020{col 76}{space 4} .2102171{col 89}{space 3} .8727579
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .9211452{col 48}{space 2} .1353174{col 59}{space 1}   -0.56{col 68}{space 3}0.576{col 76}{space 4} .6906937{col 89}{space 3} 1.228487
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 27}clinton {c |}{col 36}{res}{space 2} 3.774739{col 48}{space 2} 3.318507{col 59}{space 1}    1.51{col 68}{space 3}0.131{col 76}{space 4} .6738537{col 89}{space 3} 21.14503
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} 2.262917{col 48}{space 2} 1.419049{col 59}{space 1}    1.30{col 68}{space 3}0.193{col 76}{space 4} .6620486{col 89}{space 3} 7.734768
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       614{col 28}-3286.332{col 39}-3070.141{col 50}    40{col 58} 6220.282{col 69} 6397.082
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF2B
{txt}
{com}. estout modelF2B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF2B   
{txt}                     b/se   
{txt}{hline 28}
{txt}zloyalmedian{res}        1.385*  {txt}
            {res}      (0.193)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.222   {txt}
            {res}      (0.255)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.586***{txt}
            {res}      (0.095)   {txt}
{txt}zpecompmed~n{res}        0.981   {txt}
            {res}      (0.085)   {txt}
{txt}zmecompmed~n{res}        1.037   {txt}
            {res}      (0.071)   {txt}
{txt}toplevel2   {res}        0.481***{txt}
            {res}      (0.059)   {txt}
{txt}presagency~n{res}        0.721   {txt}
            {res}      (0.260)   {txt}
{txt}presagency~d{res}        0.720   {txt}
            {res}      (0.265)   {txt}
{txt}subagencyd~n{res}        1.479   {txt}
            {res}      (0.402)   {txt}
{txt}standalone~n{res}        1.495   {txt}
            {res}      (0.537)   {txt}
{txt}okstartsen~n{res}        0.000   {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}       76.806*  {txt}
            {res}    (159.040)   {txt}
{txt}okcrossover {res}        0.216***{txt}
            {res}      (0.039)   {txt}
{txt}okstartpre~p{res}        0.998   {txt}
            {res}      (0.006)   {txt}
{txt}okstartune~t{res}        1.030   {txt}
            {res}      (0.106)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.487   {txt}
            {res}      (0.494)   {txt}
{txt}3.okstarta~r{res}        3.417***{txt}
            {res}      (1.113)   {txt}
{txt}4.okstarta~r{res}        1.740   {txt}
            {res}      (0.645)   {txt}
{txt}5.okstarta~r{res}        1.336   {txt}
            {res}      (0.329)   {txt}
{txt}6.okstarta~r{res}        2.865***{txt}
            {res}      (0.698)   {txt}
{txt}7.okstarta~r{res}        5.408***{txt}
            {res}      (1.656)   {txt}
{txt}8.okstarta~r{res}        7.716***{txt}
            {res}      (3.265)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        2.320*  {txt}
            {res}      (0.847)   {txt}
{txt}3.sbagency  {res}        2.170*  {txt}
            {res}      (0.770)   {txt}
{txt}4.sbagency  {res}        1.415   {txt}
            {res}      (0.399)   {txt}
{txt}5.sbagency  {res}        1.132   {txt}
            {res}      (0.336)   {txt}
{txt}6.sbagency  {res}        3.141***{txt}
            {res}      (0.745)   {txt}
{txt}7.sbagency  {res}        1.482   {txt}
            {res}      (0.567)   {txt}
{txt}8.sbagency  {res}        2.142*  {txt}
            {res}      (0.738)   {txt}
{txt}9.sbagency  {res}        1.910*  {txt}
            {res}      (0.603)   {txt}
{txt}11.sbagency {res}        3.957***{txt}
            {res}      (1.549)   {txt}
{txt}12.sbagency {res}        1.790*  {txt}
            {res}      (0.505)   {txt}
{txt}13.sbagency {res}        1.853   {txt}
            {res}      (0.603)   {txt}
{txt}14.sbagency {res}        2.169*  {txt}
            {res}      (0.773)   {txt}
{txt}15.sbagency {res}        1.584   {txt}
            {res}      (0.586)   {txt}
{txt}16.sbagency {res}        0.767   {txt}
            {res}      (0.158)   {txt}
{txt}17.sbagency {res}        1.244*  {txt}
            {res}      (0.112)   {txt}
{txt}18.sbagency {res}        2.030   {txt}
            {res}      (0.774)   {txt}
{txt}19.sbagency {res}        0.855   {txt}
            {res}      (0.146)   {txt}
{txt}20.sbagency {res}        0.287** {txt}
            {res}      (0.109)   {txt}
{txt}21.sbagency {res}        1.330   {txt}
            {res}      (0.201)   {txt}
{txt}22.sbagency {res}        0.526   {txt}
            {res}      (0.236)   {txt}
{txt}23.sbagency {res}        1.414   {txt}
            {res}      (0.501)   {txt}
{txt}24.sbagency {res}        0.549   {txt}
            {res}      (0.225)   {txt}
{txt}25.sbagency {res}        1.864***{txt}
            {res}      (0.273)   {txt}
{txt}26.sbagency {res}        0.638*  {txt}
            {res}      (0.114)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        1.051   {txt}
            {res}      (0.174)   {txt}
{txt}29.sbagency {res}        5.355***{txt}
            {res}      (2.200)   {txt}
{txt}30.sbagency {res}        1.552   {txt}
            {res}      (0.494)   {txt}
{txt}50.sbagency {res}        2.295***{txt}
            {res}      (0.541)   {txt}
{txt}51.sbagency {res}        2.863***{txt}
            {res}      (0.797)   {txt}
{txt}52.sbagency {res}        1.602   {txt}
            {res}      (0.600)   {txt}
{txt}53.sbagency {res}        1.555   {txt}
            {res}      (0.382)   {txt}
{txt}54.sbagency {res}        2.371***{txt}
            {res}      (0.527)   {txt}
{txt}55.sbagency {res}        1.275   {txt}
            {res}      (0.484)   {txt}
{txt}56.sbagency {res}        0.763   {txt}
            {res}      (0.339)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.340   {txt}
            {res}      (0.477)   {txt}
{txt}59.sbagency {res}        0.428*  {txt}
            {res}      (0.156)   {txt}
{txt}60.sbagency {res}        0.921   {txt}
            {res}      (0.135)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}clinton     {res}        3.775   {txt}
            {res}      (3.319)   {txt}
{txt}bush43      {res}        2.263   {txt}
            {res}      (1.419)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  1.3600037  [0.9816979 - (-0.3783058)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3600037, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3600037

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .4828945{col 26}{space 2} .1063266{col 37}{space 1}   -3.31{col 46}{space 3}0.001{col 54}{space 4} .3136387{col 67}{space 3} .7434895
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF2Bzloyal = r(table)
{txt}
{com}. mat list modelF2Bzloyal
{res}
{txt}modelF2Bzloyal[9,1]
               (1)
     b {res}   .4828945
{txt}    se {res}  .10632655
{txt}     z {res} -3.3061024
{txt}pvalue {res}  .00094604
{txt}    ll {res}  .31363874
{txt}    ul {res}  .74348947
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL F3A: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr if carter==1 | bush41==1,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-284.07803
{txt}Iteration 1:   log pseudolikelihood = {res}-233.25087
{txt}Iteration 2:   log pseudolikelihood = {res} -232.2327
{txt}Iteration 3:   log pseudolikelihood = {res}-232.23269

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.23269}  
Iteration 1:{space 3}log pseudolikelihood = {res:-136.25299}  
Iteration 2:{space 3}log pseudolikelihood = {res:-97.962533}  
Iteration 3:{space 3}log pseudolikelihood = {res:-91.669351}  
Iteration 4:{space 3}log pseudolikelihood = {res:-91.518177}  
Iteration 5:{space 3}log pseudolikelihood = {res:-91.517746}  
Iteration 6:{space 3}log pseudolikelihood = {res:-91.517746}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
                                                {txt}Wald chi2({res}18{txt})    =  {res}    442.04
{txt}Log pseudolikelihood =   {res}-91.517746             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.415167{col 48}{space 2} .2046358{col 59}{space 1}    2.40{col 68}{space 3}0.016{col 76}{space 4} 1.065914{col 89}{space 3} 1.878854
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.556993{col 48}{space 2} .2794989{col 59}{space 1}    2.47{col 68}{space 3}0.014{col 76}{space 4} 1.095181{col 89}{space 3} 2.213541
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6691834{col 48}{space 2} .1044269{col 59}{space 1}   -2.57{col 68}{space 3}0.010{col 76}{space 4} .4928492{col 89}{space 3} .9086074
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .8650707{col 48}{space 2} .0899674{col 59}{space 1}   -1.39{col 68}{space 3}0.163{col 76}{space 4} .7055481{col 89}{space 3} 1.060661
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.151136{col 48}{space 2} .1241901{col 59}{space 1}    1.30{col 68}{space 3}0.192{col 76}{space 4} .9317408{col 89}{space 3} 1.422193
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5336867{col 48}{space 2} .0581527{col 59}{space 1}   -5.76{col 68}{space 3}0.000{col 76}{space 4} .4310582{col 89}{space 3} .6607493
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.273105{col 48}{space 2} .1839657{col 59}{space 1}    1.67{col 68}{space 3}0.095{col 76}{space 4} .9591006{col 89}{space 3} 1.689912
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.104522{col 48}{space 2} .1816415{col 59}{space 1}    0.60{col 68}{space 3}0.546{col 76}{space 4} .8001879{col 89}{space 3} 1.524602
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.991117{col 48}{space 2} .4750344{col 59}{space 1}    2.89{col 68}{space 3}0.004{col 76}{space 4}  1.24744{col 89}{space 3} 3.178145
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} .6590571{col 48}{space 2} .1533233{col 59}{space 1}   -1.79{col 68}{space 3}0.073{col 76}{space 4} .4177334{col 89}{space 3} 1.039793
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 2.2e-186{col 48}{space 2} 2.1e-184{col 59}{space 1}   -4.48{col 68}{space 3}0.000{col 76}{space 4} 1.5e-267{col 89}{space 3} 3.3e-105
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1.06e+15{col 48}{space 2} 8.39e+15{col 59}{space 1}    4.36{col 68}{space 3}0.000{col 76}{space 4} 1.85e+08{col 89}{space 3} 6.05e+21
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .0320449{col 48}{space 2} .0191921{col 59}{space 1}   -5.74{col 68}{space 3}0.000{col 76}{space 4} .0099075{col 89}{space 3} .1036464
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9956557{col 48}{space 2} .0085946{col 59}{space 1}   -0.50{col 68}{space 3}0.614{col 76}{space 4} .9789523{col 89}{space 3} 1.012644
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 2.294834{col 48}{space 2} .4895267{col 59}{space 1}    3.89{col 68}{space 3}0.000{col 76}{space 4} 1.510691{col 89}{space 3} 3.485996
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2}  3.74723{col 48}{space 2} .8461271{col 59}{space 1}    5.85{col 68}{space 3}0.000{col 76}{space 4} 2.407179{col 89}{space 3} 5.833272
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 9.334606{col 48}{space 2} 2.025324{col 59}{space 1}   10.30{col 68}{space 3}0.000{col 76}{space 4} 6.101144{col 89}{space 3} 14.28173
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 17.98028{col 48}{space 2} 7.547219{col 59}{space 1}    6.88{col 68}{space 3}0.000{col 76}{space 4} 7.897776{col 89}{space 3} 40.93437
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 1.18e+90{col 48}{space 2} 6.02e+91{col 59}{space 1}    4.07{col 68}{space 3}0.000{col 76}{space 4} 4.63e+46{col 89}{space 3} 3.0e+133
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} 1.237387{col 48}{space 2} .0694392{col 59}{space 1}   17.82{col 68}{space 3}0.000{col 76}{space 4} 1.101288{col 89}{space 3} 1.373485
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 3.446595{col 48}{space 2} .2393288{col 76}{space 4} 3.008039{col 89}{space 3} 3.949089
{txt}                               1/p {c |}{col 36}{res}{space 2} .2901415{col 48}{space 2} .0201472{col 76}{space 4} .2532229{col 89}{space 3} .3324425
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       246{col 28}-232.2327{col 39}-91.51775{col 50}    20{col 58} 223.0355{col 69} 293.1421
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF3A
{txt}
{com}. estout modelF3A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF3A   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.415*  {txt}
            {res}      (0.205)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.557*  {txt}
            {res}      (0.279)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.669*  {txt}
            {res}      (0.104)   {txt}
{txt}zpecompmed~n{res}        0.865   {txt}
            {res}      (0.090)   {txt}
{txt}zmecompmed~n{res}        1.151   {txt}
            {res}      (0.124)   {txt}
{txt}toplevel2   {res}        0.534***{txt}
            {res}      (0.058)   {txt}
{txt}presagency~n{res}        1.273   {txt}
            {res}      (0.184)   {txt}
{txt}presagency~d{res}        1.105   {txt}
            {res}      (0.182)   {txt}
{txt}subagencyd~n{res}        1.991** {txt}
            {res}      (0.475)   {txt}
{txt}standalone~n{res}        0.659   {txt}
            {res}      (0.153)   {txt}
{txt}okstartsen~n{res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}    1.057e+15***{txt}
            {res}  (8.390e+15)   {txt}
{txt}okcrossover {res}        0.032***{txt}
            {res}      (0.019)   {txt}
{txt}okstartpre~p{res}        0.996   {txt}
            {res}      (0.009)   {txt}
{txt}okstartune~t{res}        2.295***{txt}
            {res}      (0.490)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        3.747***{txt}
            {res}      (0.846)   {txt}
{txt}3.okstarta~r{res}        9.335***{txt}
            {res}      (2.025)   {txt}
{txt}4.okstarta~r{res}       17.980***{txt}
            {res}      (7.547)   {txt}
{txt}_cons       {res}    1.181e+90***{txt}
            {res}  (6.023e+91)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        3.447***{txt}
            {res}      (0.239)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. *** NOTE: IQR = 0.8404716 [0.4018788 - (-0.4385928)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.8404716, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.8404716

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7134699{col 26}{space 2} .0935763{col 37}{space 1}   -2.57{col 46}{space 3}0.010{col 54}{space 4} .5517406{col 67}{space 3} .9226063
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF3Azloyal = r(table)
{txt}
{com}. mat list modelF3Azloyal
{res}
{txt}modelF3Azloyal[9,1]
               (1)
     b {res}  .71346991
{txt}    se {res}  .09357634
{txt}     z {res} -2.5741353
{txt}pvalue {res}   .0100491
{txt}    ll {res}  .55174055
{txt}    ul {res}  .92260631
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure F2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. 
. 
. 
. ** Generate 'manual' interaction variable ** 
. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model F3A  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  if carter==1 | bush41==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-284.07803
{txt}Iteration 1:   log pseudolikelihood = {res}-233.25087
{txt}Iteration 2:   log pseudolikelihood = {res} -232.2327
{txt}Iteration 3:   log pseudolikelihood = {res}-232.23269

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.23269}  
Iteration 1:{space 3}log pseudolikelihood = {res:-136.25299}  
Iteration 2:{space 3}log pseudolikelihood = {res:-97.962533}  
Iteration 3:{space 3}log pseudolikelihood = {res:-91.669351}  
Iteration 4:{space 3}log pseudolikelihood = {res:-91.518177}  
Iteration 5:{space 3}log pseudolikelihood = {res:-91.517746}  
Iteration 6:{space 3}log pseudolikelihood = {res:-91.517746}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
                                                {txt}Wald chi2({res}18{txt})    =  {res}    442.04
{txt}Log pseudolikelihood =   {res}-91.517746             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.415167{col 40}{space 2} .2046358{col 51}{space 1}    2.40{col 60}{space 3}0.016{col 68}{space 4} 1.065914{col 81}{space 3} 1.878854
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.556993{col 40}{space 2} .2794989{col 51}{space 1}    2.47{col 60}{space 3}0.014{col 68}{space 4} 1.095181{col 81}{space 3} 2.213541
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6691834{col 40}{space 2} .1044269{col 51}{space 1}   -2.57{col 60}{space 3}0.010{col 68}{space 4} .4928492{col 81}{space 3} .9086074
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .8650707{col 40}{space 2} .0899674{col 51}{space 1}   -1.39{col 60}{space 3}0.163{col 68}{space 4} .7055481{col 81}{space 3} 1.060661
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.151136{col 40}{space 2} .1241901{col 51}{space 1}    1.30{col 60}{space 3}0.192{col 68}{space 4} .9317408{col 81}{space 3} 1.422193
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5336867{col 40}{space 2} .0581527{col 51}{space 1}   -5.76{col 60}{space 3}0.000{col 68}{space 4} .4310582{col 81}{space 3} .6607493
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.273105{col 40}{space 2} .1839657{col 51}{space 1}    1.67{col 60}{space 3}0.095{col 68}{space 4} .9591006{col 81}{space 3} 1.689912
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.104522{col 40}{space 2} .1816415{col 51}{space 1}    0.60{col 60}{space 3}0.546{col 68}{space 4} .8001879{col 81}{space 3} 1.524602
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.991117{col 40}{space 2} .4750344{col 51}{space 1}    2.89{col 60}{space 3}0.004{col 68}{space 4}  1.24744{col 81}{space 3} 3.178145
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} .6590571{col 40}{space 2} .1533233{col 51}{space 1}   -1.79{col 60}{space 3}0.073{col 68}{space 4} .4177334{col 81}{space 3} 1.039793
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 2.2e-186{col 40}{space 2} 2.1e-184{col 51}{space 1}   -4.48{col 60}{space 3}0.000{col 68}{space 4} 1.5e-267{col 81}{space 3} 3.3e-105
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 1.06e+15{col 40}{space 2} 8.39e+15{col 51}{space 1}    4.36{col 60}{space 3}0.000{col 68}{space 4} 1.85e+08{col 81}{space 3} 6.05e+21
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .0320449{col 40}{space 2} .0191921{col 51}{space 1}   -5.74{col 60}{space 3}0.000{col 68}{space 4} .0099075{col 81}{space 3} .1036464
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9956557{col 40}{space 2} .0085946{col 51}{space 1}   -0.50{col 60}{space 3}0.614{col 68}{space 4} .9789523{col 81}{space 3} 1.012644
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 2.294834{col 40}{space 2} .4895267{col 51}{space 1}    3.89{col 60}{space 3}0.000{col 68}{space 4} 1.510691{col 81}{space 3} 3.485996
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2}  3.74723{col 40}{space 2} .8461271{col 51}{space 1}    5.85{col 60}{space 3}0.000{col 68}{space 4} 2.407179{col 81}{space 3} 5.833272
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 9.334606{col 40}{space 2} 2.025324{col 51}{space 1}   10.30{col 60}{space 3}0.000{col 68}{space 4} 6.101144{col 81}{space 3} 14.28173
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 17.98028{col 40}{space 2} 7.547219{col 51}{space 1}    6.88{col 60}{space 3}0.000{col 68}{space 4} 7.897776{col 81}{space 3} 40.93437
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 1.18e+90{col 40}{space 2} 6.02e+91{col 51}{space 1}    4.07{col 60}{space 3}0.000{col 68}{space 4} 4.63e+46{col 81}{space 3} 3.0e+133
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} 1.237387{col 40}{space 2} .0694392{col 51}{space 1}   17.82{col 60}{space 3}0.000{col 68}{space 4} 1.101288{col 81}{space 3} 1.373485
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 3.446595{col 40}{space 2} .2393288{col 68}{space 4} 3.008039{col 81}{space 3} 3.949089
{txt}                       1/p {c |}{col 28}{res}{space 2} .2901415{col 40}{space 2} .0201472{col 68}{space 4} .2532229{col 81}{space 3} .3324425
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelF3Aa
{txt}
{com}. 
. 
. margins, predict(median time) at(loyalppdiff=(-0.4385928  0.4018788))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4385928}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.4018788}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 864.7782{col 26}{space 2} 32.99834{col 37}{space 1}   26.21{col 46}{space 3}0.000{col 54}{space 4} 800.1026{col 67}{space 3} 929.4538
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 953.7763{col 26}{space 2} 39.59414{col 37}{space 1}   24.09{col 46}{space 3}0.000{col 54}{space 4} 876.1732{col 67}{space 3} 1031.379
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.4385928  0.401878))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4385928}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}.401878}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     6.30{col 38}{space 2}   0.0121
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 88.99802{col 26}{space 2}  35.4524{col 37}{space 5} 19.51259{col 51}{space 3} 158.4835
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelF3Aazloyal = r(table)
{txt}
{com}. mat list modelF3Aazloyal
{res}
{txt}modelF3Aazloyal[9,1]
            r2vs1.
              _at
     b {res} 88.998024
{txt}    se {res} 35.452402
{txt}     z {res} 2.5103524
{txt}pvalue {res} .01206107
{txt}    ll {res} 19.512593
{txt}    ul {res} 158.48345
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. estimates restore modelF3Aa
{txt}(results {stata estimates replay modelF3Aa:modelF3Aa} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.8003148 1.753017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.8003148}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.753017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 829.0784{col 26}{space 2} 39.35772{col 37}{space 1}   21.07{col 46}{space 3}0.000{col 54}{space 4} 751.9387{col 67}{space 3} 906.2181
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1116.443{col 26}{space 2} 102.1225{col 37}{space 1}   10.93{col 46}{space 3}0.000{col 54}{space 4} 916.2866{col 67}{space 3} 1316.599
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.8003148 1.753017))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.8003148}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.753017}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     5.58{col 38}{space 2}   0.0182
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 287.3645{col 26}{space 2} 121.7001{col 37}{space 5}  48.8367{col 51}{space 3} 525.8923
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF3Abzloyal = r(table)
{txt}
{com}. mat list modelF3Abzloyal
{res}
{txt}modelF3Abzloyal[9,1]
            r2vs1.
              _at
     b {res} 287.36453
{txt}    se {res} 121.70011
{txt}     z {res} 2.3612512
{txt}pvalue {res} .01821339
{txt}    ll {res} 48.836702
{txt}    ul {res} 525.89235
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** MODEL F3B: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr if reagan==1 | clinton==1 | bush43==1,   distribution(weibull)  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-726.47593
{txt}Iteration 1:   log pseudolikelihood = {res}-593.65309
{txt}Iteration 2:   log pseudolikelihood = {res}-589.88411
{txt}Iteration 3:   log pseudolikelihood = {res}-589.88292
{txt}Iteration 4:   log pseudolikelihood = {res}-589.88292

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-589.88292}  
Iteration 1:{space 3}log pseudolikelihood = {res:-476.95551}  
Iteration 2:{space 3}log pseudolikelihood = {res: -396.0689}  
Iteration 3:{space 3}log pseudolikelihood = {res: -394.3509}  
Iteration 4:{space 3}log pseudolikelihood = {res:-394.34134}  
Iteration 5:{space 3}log pseudolikelihood = {res:-394.34134}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
                                                {txt}Wald chi2({res}22{txt})    =  {res}    942.46
{txt}Log pseudolikelihood =   {res}-394.34134             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.453795{col 48}{space 2} .1589283{col 59}{space 1}    3.42{col 68}{space 3}0.001{col 76}{space 4} 1.173411{col 89}{space 3} 1.801177
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.154413{col 48}{space 2} .1549687{col 59}{space 1}    1.07{col 68}{space 3}0.285{col 76}{space 4} .8873514{col 89}{space 3} 1.501851
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6126476{col 48}{space 2} .0785071{col 59}{space 1}   -3.82{col 68}{space 3}0.000{col 76}{space 4} .4765785{col 89}{space 3} .7875663
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.012878{col 48}{space 2} .0764906{col 59}{space 1}    0.17{col 68}{space 3}0.865{col 76}{space 4} .8735262{col 89}{space 3}  1.17446
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.008976{col 48}{space 2} .0646202{col 59}{space 1}    0.14{col 68}{space 3}0.889{col 76}{space 4} .8899497{col 89}{space 3} 1.143922
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5760629{col 48}{space 2} .0533318{col 59}{space 1}   -5.96{col 68}{space 3}0.000{col 76}{space 4} .4804695{col 89}{space 3} .6906754
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.534188{col 48}{space 2} .1522428{col 59}{space 1}    4.31{col 68}{space 3}0.000{col 76}{space 4} 1.263022{col 89}{space 3} 1.863572
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.442865{col 48}{space 2} .1620653{col 59}{space 1}    3.26{col 68}{space 3}0.001{col 76}{space 4} 1.157757{col 89}{space 3} 1.798185
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.018573{col 48}{space 2} .1624685{col 59}{space 1}    0.12{col 68}{space 3}0.908{col 76}{space 4} .7451102{col 89}{space 3}   1.3924
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2}   .81497{col 48}{space 2} .1116093{col 59}{space 1}   -1.49{col 68}{space 3}0.135{col 76}{space 4} .6231181{col 89}{space 3} 1.065891
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} .0007561{col 48}{space 2}  .002933{col 59}{space 1}   -1.85{col 68}{space 3}0.064{col 76}{space 4} 3.77e-07{col 89}{space 3} 1.515488
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2}  3.77552{col 48}{space 2} 1.633246{col 59}{space 1}    3.07{col 68}{space 3}0.002{col 76}{space 4} 1.617174{col 89}{space 3} 8.814478
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2502795{col 48}{space 2} .0412674{col 59}{space 1}   -8.40{col 68}{space 3}0.000{col 76}{space 4} .1811653{col 89}{space 3} .3457608
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.001041{col 48}{space 2} .0041285{col 59}{space 1}    0.25{col 68}{space 3}0.801{col 76}{space 4} .9929817{col 89}{space 3} 1.009165
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} .9086608{col 48}{space 2} .0710993{col 59}{space 1}   -1.22{col 68}{space 3}0.221{col 76}{space 4} .7794683{col 89}{space 3} 1.059266
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.821765{col 48}{space 2} .5066658{col 59}{space 1}    2.16{col 68}{space 3}0.031{col 76}{space 4} 1.056228{col 89}{space 3}  3.14215
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.358136{col 48}{space 2}  .964746{col 59}{space 1}    6.65{col 68}{space 3}0.000{col 76}{space 4} 2.824055{col 89}{space 3} 6.725559
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.268873{col 48}{space 2}  .654663{col 59}{space 1}    2.84{col 68}{space 3}0.005{col 76}{space 4} 1.288854{col 89}{space 3}  3.99408
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.185903{col 48}{space 2} .1851086{col 59}{space 1}    1.09{col 68}{space 3}0.275{col 76}{space 4} .8733422{col 89}{space 3} 1.610326
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.356789{col 48}{space 2}  .310989{col 59}{space 1}    6.50{col 68}{space 3}0.000{col 76}{space 4} 1.819704{col 89}{space 3} 3.052394
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 4.881732{col 48}{space 2} 1.100367{col 59}{space 1}    7.03{col 68}{space 3}0.000{col 76}{space 4} 3.138404{col 89}{space 3} 7.593447
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 6.509238{col 48}{space 2} 1.994103{col 59}{space 1}    6.11{col 68}{space 3}0.000{col 76}{space 4} 3.570794{col 89}{space 3} 11.86576
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 1.34e-06{col 48}{space 2} 4.04e-06{col 59}{space 1}   -4.49{col 68}{space 3}0.000{col 76}{space 4} 3.66e-09{col 89}{space 3}  .000491
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9129112{col 48}{space 2} .0296952{col 59}{space 1}   30.74{col 68}{space 3}0.000{col 76}{space 4} .8547096{col 89}{space 3} .9711128
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.491565{col 48}{space 2} .0739876{col 76}{space 4} 2.350692{col 89}{space 3} 2.640882
{txt}                               1/p {c |}{col 36}{res}{space 2} .4013541{col 48}{space 2} .0119183{col 76}{space 4} .3786614{col 89}{space 3} .4254067
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       614{col 28}-589.8829{col 39}-394.3413{col 50}    24{col 58} 836.6827{col 69} 942.7626
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF3B
{txt}
{com}. estout modelF3B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF3B   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.454***{txt}
            {res}      (0.159)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.154   {txt}
            {res}      (0.155)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.613***{txt}
            {res}      (0.079)   {txt}
{txt}zpecompmed~n{res}        1.013   {txt}
            {res}      (0.076)   {txt}
{txt}zmecompmed~n{res}        1.009   {txt}
            {res}      (0.065)   {txt}
{txt}toplevel2   {res}        0.576***{txt}
            {res}      (0.053)   {txt}
{txt}presagency~n{res}        1.534***{txt}
            {res}      (0.152)   {txt}
{txt}presagency~d{res}        1.443** {txt}
            {res}      (0.162)   {txt}
{txt}subagencyd~n{res}        1.019   {txt}
            {res}      (0.162)   {txt}
{txt}standalone~n{res}        0.815   {txt}
            {res}      (0.112)   {txt}
{txt}okstartsen~n{res}        0.001   {txt}
            {res}      (0.003)   {txt}
{txt}okstartfil~e{res}        3.776** {txt}
            {res}      (1.633)   {txt}
{txt}okcrossover {res}        0.250***{txt}
            {res}      (0.041)   {txt}
{txt}okstartpre~p{res}        1.001   {txt}
            {res}      (0.004)   {txt}
{txt}okstartune~t{res}        0.909   {txt}
            {res}      (0.071)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.822*  {txt}
            {res}      (0.507)   {txt}
{txt}3.okstarta~r{res}        4.358***{txt}
            {res}      (0.965)   {txt}
{txt}4.okstarta~r{res}        2.269** {txt}
            {res}      (0.655)   {txt}
{txt}5.okstarta~r{res}        1.186   {txt}
            {res}      (0.185)   {txt}
{txt}6.okstarta~r{res}        2.357***{txt}
            {res}      (0.311)   {txt}
{txt}7.okstarta~r{res}        4.882***{txt}
            {res}      (1.100)   {txt}
{txt}8.okstarta~r{res}        6.509***{txt}
            {res}      (1.994)   {txt}
{txt}_cons       {res}        0.000***{txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.492***{txt}
            {res}      (0.074)   {txt}
{txt}{hline 28}

{com}. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR =  1.3600037  [0.9816979 - (-0.3783058)] ***
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian* 1.3600037, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3600037

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5135773{col 26}{space 2} .0895044{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4} .3649737{col 67}{space 3} .7226868
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF3Bzloyal = r(table)
{txt}
{com}. mat list modelF3Bzloyal
{res}
{txt}modelF3Bzloyal[9,1]
               (1)
     b {res}  .51357735
{txt}    se {res}  .08950436
{txt}     z {res} -3.8235529
{txt}pvalue {res}  .00013154
{txt}    ll {res}  .36497373
{txt}    ul {res}  .72268678
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure F2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3600037 [0.9816979 - (-0.3783058)] ***
. 
. 
. ** Generate 'manual' interaction variable ** 
. *generate loyalppdiff = soubinaryagency2nom*zloyalmedian
. 
. ** Re-Estimate Model F3B  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  if reagan==1 | clinton==1 | bush43==1,  distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-726.47593
{txt}Iteration 1:   log pseudolikelihood = {res}-593.65309
{txt}Iteration 2:   log pseudolikelihood = {res}-589.88411
{txt}Iteration 3:   log pseudolikelihood = {res}-589.88292
{txt}Iteration 4:   log pseudolikelihood = {res}-589.88292

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-589.88292}  
Iteration 1:{space 3}log pseudolikelihood = {res:-476.95551}  
Iteration 2:{space 3}log pseudolikelihood = {res: -396.0689}  
Iteration 3:{space 3}log pseudolikelihood = {res: -394.3509}  
Iteration 4:{space 3}log pseudolikelihood = {res:-394.34134}  
Iteration 5:{space 3}log pseudolikelihood = {res:-394.34134}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
                                                {txt}Wald chi2({res}22{txt})    =  {res}    942.46
{txt}Log pseudolikelihood =   {res}-394.34134             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.453795{col 40}{space 2} .1589283{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} 1.173411{col 81}{space 3} 1.801177
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.154413{col 40}{space 2} .1549687{col 51}{space 1}    1.07{col 60}{space 3}0.285{col 68}{space 4} .8873514{col 81}{space 3} 1.501851
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6126476{col 40}{space 2} .0785071{col 51}{space 1}   -3.82{col 60}{space 3}0.000{col 68}{space 4} .4765785{col 81}{space 3} .7875663
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.012878{col 40}{space 2} .0764906{col 51}{space 1}    0.17{col 60}{space 3}0.865{col 68}{space 4} .8735262{col 81}{space 3}  1.17446
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.008976{col 40}{space 2} .0646202{col 51}{space 1}    0.14{col 60}{space 3}0.889{col 68}{space 4} .8899497{col 81}{space 3} 1.143922
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5760629{col 40}{space 2} .0533318{col 51}{space 1}   -5.96{col 60}{space 3}0.000{col 68}{space 4} .4804695{col 81}{space 3} .6906754
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.534188{col 40}{space 2} .1522428{col 51}{space 1}    4.31{col 60}{space 3}0.000{col 68}{space 4} 1.263022{col 81}{space 3} 1.863572
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.442865{col 40}{space 2} .1620653{col 51}{space 1}    3.26{col 60}{space 3}0.001{col 68}{space 4} 1.157757{col 81}{space 3} 1.798185
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.018573{col 40}{space 2} .1624685{col 51}{space 1}    0.12{col 60}{space 3}0.908{col 68}{space 4} .7451102{col 81}{space 3}   1.3924
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2}   .81497{col 40}{space 2} .1116093{col 51}{space 1}   -1.49{col 60}{space 3}0.135{col 68}{space 4} .6231181{col 81}{space 3} 1.065891
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} .0007561{col 40}{space 2}  .002933{col 51}{space 1}   -1.85{col 60}{space 3}0.064{col 68}{space 4} 3.77e-07{col 81}{space 3} 1.515488
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2}  3.77552{col 40}{space 2} 1.633246{col 51}{space 1}    3.07{col 60}{space 3}0.002{col 68}{space 4} 1.617174{col 81}{space 3} 8.814478
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2502795{col 40}{space 2} .0412674{col 51}{space 1}   -8.40{col 60}{space 3}0.000{col 68}{space 4} .1811653{col 81}{space 3} .3457608
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} 1.001041{col 40}{space 2} .0041285{col 51}{space 1}    0.25{col 60}{space 3}0.801{col 68}{space 4} .9929817{col 81}{space 3} 1.009165
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} .9086608{col 40}{space 2} .0710993{col 51}{space 1}   -1.22{col 60}{space 3}0.221{col 68}{space 4} .7794683{col 81}{space 3} 1.059266
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.821765{col 40}{space 2} .5066658{col 51}{space 1}    2.16{col 60}{space 3}0.031{col 68}{space 4} 1.056228{col 81}{space 3}  3.14215
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.358136{col 40}{space 2}  .964746{col 51}{space 1}    6.65{col 60}{space 3}0.000{col 68}{space 4} 2.824055{col 81}{space 3} 6.725559
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 2.268873{col 40}{space 2}  .654663{col 51}{space 1}    2.84{col 60}{space 3}0.005{col 68}{space 4} 1.288854{col 81}{space 3}  3.99408
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.185903{col 40}{space 2} .1851086{col 51}{space 1}    1.09{col 60}{space 3}0.275{col 68}{space 4} .8733422{col 81}{space 3} 1.610326
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.356789{col 40}{space 2}  .310989{col 51}{space 1}    6.50{col 60}{space 3}0.000{col 68}{space 4} 1.819704{col 81}{space 3} 3.052394
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 4.881732{col 40}{space 2} 1.100367{col 51}{space 1}    7.03{col 60}{space 3}0.000{col 68}{space 4} 3.138404{col 81}{space 3} 7.593447
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 6.509238{col 40}{space 2} 1.994103{col 51}{space 1}    6.11{col 60}{space 3}0.000{col 68}{space 4} 3.570794{col 81}{space 3} 11.86576
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 1.34e-06{col 40}{space 2} 4.04e-06{col 51}{space 1}   -4.49{col 60}{space 3}0.000{col 68}{space 4} 3.66e-09{col 81}{space 3}  .000491
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9129112{col 40}{space 2} .0296952{col 51}{space 1}   30.74{col 60}{space 3}0.000{col 68}{space 4} .8547096{col 81}{space 3} .9711128
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.491565{col 40}{space 2} .0739876{col 68}{space 4} 2.350692{col 81}{space 3} 2.640882
{txt}                       1/p {c |}{col 28}{res}{space 2} .4013541{col 40}{space 2} .0119183{col 68}{space 4} .3786614{col 81}{space 3} .4254067
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimate store modelF3Ba
{txt}
{com}. 
. 
. margins, predict(median time) at(loyalppdiff=(-0.3783058  0.9816979))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3783058}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9816979}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 940.6295{col 26}{space 2} 24.18002{col 37}{space 1}   38.90{col 46}{space 3}0.000{col 54}{space 4} 893.2375{col 67}{space 3} 988.0215
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1229.046{col 26}{space 2} 63.27328{col 37}{space 1}   19.42{col 46}{space 3}0.000{col 54}{space 4} 1105.033{col 67}{space 3} 1353.059
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.3783058  0.9816979))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3783058}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9816979}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    13.25{col 38}{space 2}   0.0003
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 288.4165{col 26}{space 2} 79.24807{col 37}{space 5} 133.0931{col 51}{space 3} 443.7398
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. matrix modelF3Bazloyal = r(table)
{txt}
{com}. mat list modelF3Bazloyal
{res}
{txt}modelF3Bazloyal[9,1]
            r2vs1.
              _at
     b {res} 288.41645
{txt}    se {res} 79.248075
{txt}     z {res} 3.6394128
{txt}pvalue {res} .00027326
{txt}    ll {res} 133.09308
{txt}    ul {res} 443.73983
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. estimates restore modelF3Ba
{txt}(results {stata estimates replay modelF3Ba:modelF3Ba} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6247732 1.690957))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6247732}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.690957}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 896.1265{col 26}{space 2} 32.58731{col 37}{space 1}   27.50{col 46}{space 3}0.000{col 54}{space 4} 832.2566{col 67}{space 3} 959.9965
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1412.998{col 26}{space 2} 122.1788{col 37}{space 1}   11.56{col 46}{space 3}0.000{col 54}{space 4} 1173.532{col 67}{space 3} 1652.464
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6247732 1.690957))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6247732}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.690957}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    11.94{col 38}{space 2}   0.0005
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 516.8715{col 26}{space 2} 149.5901{col 37}{space 5} 223.6803{col 51}{space 3} 810.0627
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF3Bbzloyal = r(table)
{txt}
{com}. mat list modelF3Bbzloyal
{res}
{txt}modelF3Bbzloyal[9,1]
            r2vs1.
              _at
     b {res} 516.87151
{txt}    se {res} 149.59011
{txt}     z {res}  3.455252
{txt}pvalue {res} .00054978
{txt}    ll {res} 223.68029
{txt}    ul {res} 810.06274
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL F4A: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency bush41 if carter==1 | bush41==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
note: bush41 omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-284.07803
{txt}Iteration 1:   log pseudolikelihood = {res}-233.25087
{txt}Iteration 2:   log pseudolikelihood = {res} -232.2327
{txt}Iteration 3:   log pseudolikelihood = {res}-232.23269

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.23269}  
Iteration 1:{space 3}log pseudolikelihood = {res:-121.23187}  
Iteration 2:{space 3}log pseudolikelihood = {res:-72.053612}  
Iteration 3:{space 3}log pseudolikelihood = {res:-57.324451}  
Iteration 4:{space 3}log pseudolikelihood = {res: -56.65668}  
Iteration 5:{space 3}log pseudolikelihood = {res:-56.649494}  
Iteration 6:{space 3}log pseudolikelihood = {res:-56.649488}  
Iteration 7:{space 3}log pseudolikelihood = {res:-56.649488}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-56.649488             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.316893{col 48}{space 2} .2328801{col 59}{space 1}    1.56{col 68}{space 3}0.120{col 76}{space 4} .9311586{col 89}{space 3} 1.862419
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.445397{col 48}{space 2} .3912769{col 59}{space 1}    1.36{col 68}{space 3}0.174{col 76}{space 4} .8502808{col 89}{space 3} 2.457039
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6621373{col 48}{space 2} .1306993{col 59}{space 1}   -2.09{col 68}{space 3}0.037{col 76}{space 4} .4497067{col 89}{space 3}  .974915
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9524642{col 48}{space 2} .1311192{col 59}{space 1}   -0.35{col 68}{space 3}0.724{col 76}{space 4} .7272263{col 89}{space 3} 1.247463
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9567524{col 48}{space 2} .1171749{col 59}{space 1}   -0.36{col 68}{space 3}0.718{col 76}{space 4} .7525782{col 89}{space 3} 1.216319
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .4769772{col 48}{space 2} .0769745{col 59}{space 1}   -4.59{col 68}{space 3}0.000{col 76}{space 4} .3476409{col 89}{space 3} .6544318
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} 1.608553{col 48}{space 2} .5506473{col 59}{space 1}    1.39{col 68}{space 3}0.165{col 76}{space 4} .8223341{col 89}{space 3} 3.146463
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} 1.441685{col 48}{space 2} .4597927{col 59}{space 1}    1.15{col 68}{space 3}0.251{col 76}{space 4} .7716124{col 89}{space 3} 2.693654
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 3.836216{col 48}{space 2} 1.245817{col 59}{space 1}    4.14{col 68}{space 3}0.000{col 76}{space 4} 2.029897{col 89}{space 3} 7.249902
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2}  1.59428{col 48}{space 2} .7620669{col 59}{space 1}    0.98{col 68}{space 3}0.329{col 76}{space 4} .6247264{col 89}{space 3} 4.068549
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 5.6e-151{col 48}{space 2} 8.0e-149{col 59}{space 1}   -2.42{col 68}{space 3}0.016{col 76}{space 4} 8.8e-273{col 89}{space 3} 3.58e-29
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 3.04e+12{col 48}{space 2} 3.68e+13{col 59}{space 1}    2.38{col 68}{space 3}0.017{col 76}{space 4} 158.1131{col 89}{space 3} 5.86e+22
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .0398369{col 48}{space 2} .0341283{col 59}{space 1}   -3.76{col 68}{space 3}0.000{col 76}{space 4} .0074312{col 89}{space 3} .2135559
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9812415{col 48}{space 2} .0104904{col 59}{space 1}   -1.77{col 68}{space 3}0.077{col 76}{space 4} .9608946{col 89}{space 3} 1.002019
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 2.575866{col 48}{space 2} .8409443{col 59}{space 1}    2.90{col 68}{space 3}0.004{col 76}{space 4} 1.358411{col 89}{space 3} 4.884448
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 3.005993{col 48}{space 2} 1.038367{col 59}{space 1}    3.19{col 68}{space 3}0.001{col 76}{space 4}  1.52741{col 89}{space 3} 5.915894
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 11.94894{col 48}{space 2} 3.506259{col 59}{space 1}    8.45{col 68}{space 3}0.000{col 76}{space 4} 6.722879{col 89}{space 3} 21.23749
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 26.88078{col 48}{space 2} 17.47868{col 59}{space 1}    5.06{col 68}{space 3}0.000{col 76}{space 4} 7.515629{col 89}{space 3} 96.14314
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 4.370269{col 48}{space 2}  1.60813{col 59}{space 1}    4.01{col 68}{space 3}0.000{col 76}{space 4} 2.124668{col 89}{space 3} 8.989286
{txt}{space 32}3  {c |}{col 36}{res}{space 2} .7552664{col 48}{space 2} .3233414{col 59}{space 1}   -0.66{col 68}{space 3}0.512{col 76}{space 4} .3263528{col 89}{space 3} 1.747885
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .2399284{col 48}{space 2} .0817555{col 59}{space 1}   -4.19{col 68}{space 3}0.000{col 76}{space 4} .1230368{col 89}{space 3} .4678733
{txt}{space 32}5  {c |}{col 36}{res}{space 2} .5540953{col 48}{space 2} .1688404{col 59}{space 1}   -1.94{col 68}{space 3}0.053{col 76}{space 4} .3049382{col 89}{space 3} 1.006832
{txt}{space 32}6  {c |}{col 36}{res}{space 2} .8082979{col 48}{space 2} .2233593{col 59}{space 1}   -0.77{col 68}{space 3}0.441{col 76}{space 4}   .47028{col 89}{space 3} 1.389269
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.288552{col 48}{space 2} .4168241{col 59}{space 1}    0.78{col 68}{space 3}0.433{col 76}{space 4} .6835232{col 89}{space 3} 2.429131
{txt}{space 32}8  {c |}{col 36}{res}{space 2}  2.49072{col 48}{space 2} .9301838{col 59}{space 1}    2.44{col 68}{space 3}0.015{col 76}{space 4} 1.197941{col 89}{space 3} 5.178625
{txt}{space 32}9  {c |}{col 36}{res}{space 2}  1.57882{col 48}{space 2} .5284859{col 59}{space 1}    1.36{col 68}{space 3}0.172{col 76}{space 4} .8192304{col 89}{space 3} 3.042702
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.415077{col 48}{space 2} .4931341{col 59}{space 1}    1.00{col 68}{space 3}0.319{col 76}{space 4} .7147404{col 89}{space 3} 2.801635
{txt}{space 31}13  {c |}{col 36}{res}{space 2} .4666232{col 48}{space 2} .1769115{col 59}{space 1}   -2.01{col 68}{space 3}0.044{col 76}{space 4} .2219469{col 89}{space 3} .9810327
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.538378{col 48}{space 2} .9087708{col 59}{space 1}    2.60{col 68}{space 3}0.009{col 76}{space 4} 1.258391{col 89}{space 3} 5.120319
{txt}{space 31}15  {c |}{col 36}{res}{space 2} .7290791{col 48}{space 2} .2735683{col 59}{space 1}   -0.84{col 68}{space 3}0.400{col 76}{space 4} .3494484{col 89}{space 3}  1.52113
{txt}{space 31}16  {c |}{col 36}{res}{space 2} 1.654349{col 48}{space 2} .3588949{col 59}{space 1}    2.32{col 68}{space 3}0.020{col 76}{space 4} 1.081352{col 89}{space 3}  2.53097
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 3.045634{col 48}{space 2} .6260475{col 59}{space 1}    5.42{col 68}{space 3}0.000{col 76}{space 4} 2.035675{col 89}{space 3} 4.556664
{txt}{space 31}18  {c |}{col 36}{res}{space 2} .9665056{col 48}{space 2} .3594831{col 59}{space 1}   -0.09{col 68}{space 3}0.927{col 76}{space 4} .4662379{col 89}{space 3} 2.003555
{txt}{space 31}19  {c |}{col 36}{res}{space 2} 1.870537{col 48}{space 2} .5773721{col 59}{space 1}    2.03{col 68}{space 3}0.042{col 76}{space 4} 1.021478{col 89}{space 3}  3.42534
{txt}{space 31}20  {c |}{col 36}{res}{space 2}  .452801{col 48}{space 2} .2631816{col 59}{space 1}   -1.36{col 68}{space 3}0.173{col 76}{space 4} .1449317{col 89}{space 3} 1.414658
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .2710928{col 48}{space 2} .0900681{col 59}{space 1}   -3.93{col 68}{space 3}0.000{col 76}{space 4} .1413559{col 89}{space 3} .5199027
{txt}{space 31}22  {c |}{col 36}{res}{space 2} 1.373508{col 48}{space 2} .7224843{col 59}{space 1}    0.60{col 68}{space 3}0.546{col 76}{space 4}  .489877{col 89}{space 3} 3.851013
{txt}{space 31}23  {c |}{col 36}{res}{space 2} 2.099938{col 48}{space 2} 1.055242{col 59}{space 1}    1.48{col 68}{space 3}0.140{col 76}{space 4} .7842747{col 89}{space 3} 5.622696
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .0242796{col 48}{space 2} .0317717{col 59}{space 1}   -2.84{col 68}{space 3}0.004{col 76}{space 4}  .001868{col 89}{space 3} .3155758
{txt}{space 31}25  {c |}{col 36}{res}{space 2} .8072485{col 48}{space 2} .2376273{col 59}{space 1}   -0.73{col 68}{space 3}0.467{col 76}{space 4} .4533584{col 89}{space 3} 1.437384
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .9453992{col 48}{space 2} .3591647{col 59}{space 1}   -0.15{col 68}{space 3}0.883{col 76}{space 4} .4489903{col 89}{space 3} 1.990643
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 2.851923{col 48}{space 2}  1.16075{col 59}{space 1}    2.57{col 68}{space 3}0.010{col 76}{space 4}  1.28438{col 89}{space 3} 6.332598
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 1.069588{col 48}{space 2} .4610951{col 59}{space 1}    0.16{col 68}{space 3}0.876{col 76}{space 4} .4594805{col 89}{space 3}  2.48981
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.292284{col 48}{space 2} .5719375{col 59}{space 1}    0.58{col 68}{space 3}0.562{col 76}{space 4} .5427927{col 89}{space 3} 3.076676
{txt}{space 31}50  {c |}{col 36}{res}{space 2} .7992572{col 48}{space 2} .2302847{col 59}{space 1}   -0.78{col 68}{space 3}0.437{col 76}{space 4}  .454397{col 89}{space 3} 1.405846
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.841378{col 48}{space 2}  .932191{col 59}{space 1}    3.18{col 68}{space 3}0.001{col 76}{space 4}  1.49372{col 89}{space 3} 5.404913
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.584075{col 48}{space 2} 1.377793{col 59}{space 1}    0.53{col 68}{space 3}0.597{col 76}{space 4} .2880181{col 89}{space 3} 8.712281
{txt}{space 31}53  {c |}{col 36}{res}{space 2}  1.23831{col 48}{space 2} .1048784{col 59}{space 1}    2.52{col 68}{space 3}0.012{col 76}{space 4} 1.048907{col 89}{space 3} 1.461913
{txt}{space 31}54  {c |}{col 36}{res}{space 2} .3372951{col 48}{space 2} .3321381{col 59}{space 1}   -1.10{col 68}{space 3}0.270{col 76}{space 4} .0489579{col 89}{space 3} 2.323792
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .4599093{col 48}{space 2}  .197993{col 59}{space 1}   -1.80{col 68}{space 3}0.071{col 76}{space 4} .1977999{col 89}{space 3} 1.069346
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 2.175009{col 48}{space 2} 2.044455{col 59}{space 1}    0.83{col 68}{space 3}0.408{col 76}{space 4} .3446297{col 89}{space 3}  13.7268
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .1099954{col 48}{space 2} .1129134{col 59}{space 1}   -2.15{col 68}{space 3}0.032{col 76}{space 4} .0147093{col 89}{space 3} .8225406
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .4281996{col 48}{space 2} .1522779{col 59}{space 1}   -2.39{col 68}{space 3}0.017{col 76}{space 4} .2132745{col 89}{space 3} .8597131
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}bush41 {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 29}_cons {c |}{col 36}{res}{space 2} 9.22e+67{col 48}{space 2} 7.09e+69{col 59}{space 1}    2.04{col 68}{space 3}0.042{col 76}{space 4} 353.2126{col 89}{space 3} 2.4e+133
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} 1.431208{col 48}{space 2}  .087695{col 59}{space 1}   16.32{col 68}{space 3}0.000{col 76}{space 4} 1.259329{col 89}{space 3} 1.603087
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 4.183751{col 48}{space 2} .3668942{col 76}{space 4} 3.523057{col 89}{space 3} 4.968348
{txt}                               1/p {c |}{col 36}{res}{space 2}   .23902{col 48}{space 2} .0209609{col 76}{space 4} .2012742{col 89}{space 3} .2838444
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       246{col 28}-232.2327{col 39}-56.64949{col 50}    16{col 58}  145.299{col 69} 201.3843
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF4A
{txt}
{com}. estout modelF4A, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF4A   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.317   {txt}
            {res}      (0.233)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.445   {txt}
            {res}      (0.391)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.662*  {txt}
            {res}      (0.131)   {txt}
{txt}zpecompmed~n{res}        0.952   {txt}
            {res}      (0.131)   {txt}
{txt}zmecompmed~n{res}        0.957   {txt}
            {res}      (0.117)   {txt}
{txt}toplevel2   {res}        0.477***{txt}
            {res}      (0.077)   {txt}
{txt}presagency~n{res}        1.609   {txt}
            {res}      (0.551)   {txt}
{txt}presagency~d{res}        1.442   {txt}
            {res}      (0.460)   {txt}
{txt}subagencyd~n{res}        3.836***{txt}
            {res}      (1.246)   {txt}
{txt}standalone~n{res}        1.594   {txt}
            {res}      (0.762)   {txt}
{txt}okstartsen~n{res}        0.000*  {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}    3.043e+12*  {txt}
            {res}  (3.677e+13)   {txt}
{txt}okcrossover {res}        0.040***{txt}
            {res}      (0.034)   {txt}
{txt}okstartpre~p{res}        0.981   {txt}
            {res}      (0.010)   {txt}
{txt}okstartune~t{res}        2.576** {txt}
            {res}      (0.841)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        3.006** {txt}
            {res}      (1.038)   {txt}
{txt}3.okstarta~r{res}       11.949***{txt}
            {res}      (3.506)   {txt}
{txt}4.okstarta~r{res}       26.881***{txt}
            {res}     (17.479)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        4.370***{txt}
            {res}      (1.608)   {txt}
{txt}3.sbagency  {res}        0.755   {txt}
            {res}      (0.323)   {txt}
{txt}4.sbagency  {res}        0.240***{txt}
            {res}      (0.082)   {txt}
{txt}5.sbagency  {res}        0.554   {txt}
            {res}      (0.169)   {txt}
{txt}6.sbagency  {res}        0.808   {txt}
            {res}      (0.223)   {txt}
{txt}7.sbagency  {res}        1.289   {txt}
            {res}      (0.417)   {txt}
{txt}8.sbagency  {res}        2.491*  {txt}
            {res}      (0.930)   {txt}
{txt}9.sbagency  {res}        1.579   {txt}
            {res}      (0.528)   {txt}
{txt}12.sbagency {res}        1.415   {txt}
            {res}      (0.493)   {txt}
{txt}13.sbagency {res}        0.467*  {txt}
            {res}      (0.177)   {txt}
{txt}14.sbagency {res}        2.538** {txt}
            {res}      (0.909)   {txt}
{txt}15.sbagency {res}        0.729   {txt}
            {res}      (0.274)   {txt}
{txt}16.sbagency {res}        1.654*  {txt}
            {res}      (0.359)   {txt}
{txt}17.sbagency {res}        3.046***{txt}
            {res}      (0.626)   {txt}
{txt}18.sbagency {res}        0.967   {txt}
            {res}      (0.359)   {txt}
{txt}19.sbagency {res}        1.871*  {txt}
            {res}      (0.577)   {txt}
{txt}20.sbagency {res}        0.453   {txt}
            {res}      (0.263)   {txt}
{txt}21.sbagency {res}        0.271***{txt}
            {res}      (0.090)   {txt}
{txt}22.sbagency {res}        1.374   {txt}
            {res}      (0.722)   {txt}
{txt}23.sbagency {res}        2.100   {txt}
            {res}      (1.055)   {txt}
{txt}24.sbagency {res}        0.024** {txt}
            {res}      (0.032)   {txt}
{txt}25.sbagency {res}        0.807   {txt}
            {res}      (0.238)   {txt}
{txt}26.sbagency {res}        0.945   {txt}
            {res}      (0.359)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        2.852*  {txt}
            {res}      (1.161)   {txt}
{txt}29.sbagency {res}        1.070   {txt}
            {res}      (0.461)   {txt}
{txt}30.sbagency {res}        1.292   {txt}
            {res}      (0.572)   {txt}
{txt}50.sbagency {res}        0.799   {txt}
            {res}      (0.230)   {txt}
{txt}51.sbagency {res}        2.841** {txt}
            {res}      (0.932)   {txt}
{txt}52.sbagency {res}        1.584   {txt}
            {res}      (1.378)   {txt}
{txt}53.sbagency {res}        1.238*  {txt}
            {res}      (0.105)   {txt}
{txt}54.sbagency {res}        0.337   {txt}
            {res}      (0.332)   {txt}
{txt}56.sbagency {res}        0.460   {txt}
            {res}      (0.198)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        2.175   {txt}
            {res}      (2.044)   {txt}
{txt}59.sbagency {res}        0.110*  {txt}
            {res}      (0.113)   {txt}
{txt}60.sbagency {res}        0.428*  {txt}
            {res}      (0.152)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}bush41      {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}_cons       {res}    9.221e+67*  {txt}
            {res}  (7.087e+69)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        4.184***{txt}
            {res}      (0.367)   {txt}
{txt}{hline 28}

{com}. 
. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 0.8404716 [0.9746053 - (-0.3853984)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*0.8404716, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*0.8404716

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7071506{col 26}{space 2} .1173168{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4} .5108542{col 67}{space 3} .9788741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF4Azloyal = r(table)
{txt}
{com}. mat list modelF4Azloyal
{res}
{txt}modelF4Azloyal[9,1]
               (1)
     b {res}  .70715061
{txt}    se {res}  .11731679
{txt}     z {res} -2.0886687
{txt}pvalue {res}  .03673756
{txt}    ll {res}  .51085422
{txt}    ul {res}  .97887414
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure F2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1A−MF4A] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 0.8404716 [0.401878 - (-0.4385928)]
. 
. ** Re-Estimate Model F4A  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency if carter==1 | bush41==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-284.07803
{txt}Iteration 1:   log pseudolikelihood = {res}-233.25087
{txt}Iteration 2:   log pseudolikelihood = {res} -232.2327
{txt}Iteration 3:   log pseudolikelihood = {res}-232.23269

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.23269}  
Iteration 1:{space 3}log pseudolikelihood = {res:-121.23187}  
Iteration 2:{space 3}log pseudolikelihood = {res:-72.053612}  
Iteration 3:{space 3}log pseudolikelihood = {res:-57.324451}  
Iteration 4:{space 3}log pseudolikelihood = {res: -56.65668}  
Iteration 5:{space 3}log pseudolikelihood = {res:-56.649494}  
Iteration 6:{space 3}log pseudolikelihood = {res:-56.649488}  
Iteration 7:{space 3}log pseudolikelihood = {res:-56.649488}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         246             {txt}Number of obs    =  {res}       246
{txt}No. of failures      = {res}         227
{txt}Time at risk         = {res}      205716
{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-56.649488             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:39} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.316893{col 40}{space 2} .2328801{col 51}{space 1}    1.56{col 60}{space 3}0.120{col 68}{space 4} .9311586{col 81}{space 3} 1.862419
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.445397{col 40}{space 2} .3912769{col 51}{space 1}    1.36{col 60}{space 3}0.174{col 68}{space 4} .8502808{col 81}{space 3} 2.457039
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6621373{col 40}{space 2} .1306993{col 51}{space 1}   -2.09{col 60}{space 3}0.037{col 68}{space 4} .4497067{col 81}{space 3}  .974915
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9524642{col 40}{space 2} .1311192{col 51}{space 1}   -0.35{col 60}{space 3}0.724{col 68}{space 4} .7272263{col 81}{space 3} 1.247463
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9567524{col 40}{space 2} .1171749{col 51}{space 1}   -0.36{col 60}{space 3}0.718{col 68}{space 4} .7525782{col 81}{space 3} 1.216319
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .4769772{col 40}{space 2} .0769745{col 51}{space 1}   -4.59{col 60}{space 3}0.000{col 68}{space 4} .3476409{col 81}{space 3} .6544318
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} 1.608553{col 40}{space 2} .5506473{col 51}{space 1}    1.39{col 60}{space 3}0.165{col 68}{space 4} .8223341{col 81}{space 3} 3.146463
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} 1.441685{col 40}{space 2} .4597927{col 51}{space 1}    1.15{col 60}{space 3}0.251{col 68}{space 4} .7716124{col 81}{space 3} 2.693654
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 3.836216{col 40}{space 2} 1.245817{col 51}{space 1}    4.14{col 60}{space 3}0.000{col 68}{space 4} 2.029897{col 81}{space 3} 7.249902
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2}  1.59428{col 40}{space 2} .7620669{col 51}{space 1}    0.98{col 60}{space 3}0.329{col 68}{space 4} .6247264{col 81}{space 3} 4.068549
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 5.6e-151{col 40}{space 2} 8.0e-149{col 51}{space 1}   -2.42{col 60}{space 3}0.016{col 68}{space 4} 8.8e-273{col 81}{space 3} 3.58e-29
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 3.04e+12{col 40}{space 2} 3.68e+13{col 51}{space 1}    2.38{col 60}{space 3}0.017{col 68}{space 4} 158.1131{col 81}{space 3} 5.86e+22
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .0398369{col 40}{space 2} .0341283{col 51}{space 1}   -3.76{col 60}{space 3}0.000{col 68}{space 4} .0074312{col 81}{space 3} .2135559
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9812415{col 40}{space 2} .0104904{col 51}{space 1}   -1.77{col 60}{space 3}0.077{col 68}{space 4} .9608946{col 81}{space 3} 1.002019
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 2.575866{col 40}{space 2} .8409443{col 51}{space 1}    2.90{col 60}{space 3}0.004{col 68}{space 4} 1.358411{col 81}{space 3} 4.884448
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 3.005993{col 40}{space 2} 1.038367{col 51}{space 1}    3.19{col 60}{space 3}0.001{col 68}{space 4}  1.52741{col 81}{space 3} 5.915894
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 11.94894{col 40}{space 2} 3.506259{col 51}{space 1}    8.45{col 60}{space 3}0.000{col 68}{space 4} 6.722879{col 81}{space 3} 21.23749
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 26.88078{col 40}{space 2} 17.47868{col 51}{space 1}    5.06{col 60}{space 3}0.000{col 68}{space 4} 7.515629{col 81}{space 3} 96.14314
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 4.370269{col 40}{space 2}  1.60813{col 51}{space 1}    4.01{col 60}{space 3}0.000{col 68}{space 4} 2.124668{col 81}{space 3} 8.989286
{txt}{space 24}3  {c |}{col 28}{res}{space 2} .7552664{col 40}{space 2} .3233414{col 51}{space 1}   -0.66{col 60}{space 3}0.512{col 68}{space 4} .3263528{col 81}{space 3} 1.747885
{txt}{space 24}4  {c |}{col 28}{res}{space 2} .2399284{col 40}{space 2} .0817555{col 51}{space 1}   -4.19{col 60}{space 3}0.000{col 68}{space 4} .1230368{col 81}{space 3} .4678733
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .5540953{col 40}{space 2} .1688404{col 51}{space 1}   -1.94{col 60}{space 3}0.053{col 68}{space 4} .3049382{col 81}{space 3} 1.006832
{txt}{space 24}6  {c |}{col 28}{res}{space 2} .8082979{col 40}{space 2} .2233593{col 51}{space 1}   -0.77{col 60}{space 3}0.441{col 68}{space 4}   .47028{col 81}{space 3} 1.389269
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.288552{col 40}{space 2} .4168241{col 51}{space 1}    0.78{col 60}{space 3}0.433{col 68}{space 4} .6835232{col 81}{space 3} 2.429131
{txt}{space 24}8  {c |}{col 28}{res}{space 2}  2.49072{col 40}{space 2} .9301838{col 51}{space 1}    2.44{col 60}{space 3}0.015{col 68}{space 4} 1.197941{col 81}{space 3} 5.178625
{txt}{space 24}9  {c |}{col 28}{res}{space 2}  1.57882{col 40}{space 2} .5284859{col 51}{space 1}    1.36{col 60}{space 3}0.172{col 68}{space 4} .8192304{col 81}{space 3} 3.042702
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.415077{col 40}{space 2} .4931341{col 51}{space 1}    1.00{col 60}{space 3}0.319{col 68}{space 4} .7147404{col 81}{space 3} 2.801635
{txt}{space 23}13  {c |}{col 28}{res}{space 2} .4666232{col 40}{space 2} .1769115{col 51}{space 1}   -2.01{col 60}{space 3}0.044{col 68}{space 4} .2219469{col 81}{space 3} .9810327
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.538378{col 40}{space 2} .9087708{col 51}{space 1}    2.60{col 60}{space 3}0.009{col 68}{space 4} 1.258391{col 81}{space 3} 5.120319
{txt}{space 23}15  {c |}{col 28}{res}{space 2} .7290791{col 40}{space 2} .2735683{col 51}{space 1}   -0.84{col 60}{space 3}0.400{col 68}{space 4} .3494484{col 81}{space 3}  1.52113
{txt}{space 23}16  {c |}{col 28}{res}{space 2} 1.654349{col 40}{space 2} .3588949{col 51}{space 1}    2.32{col 60}{space 3}0.020{col 68}{space 4} 1.081352{col 81}{space 3}  2.53097
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 3.045634{col 40}{space 2} .6260475{col 51}{space 1}    5.42{col 60}{space 3}0.000{col 68}{space 4} 2.035675{col 81}{space 3} 4.556664
{txt}{space 23}18  {c |}{col 28}{res}{space 2} .9665056{col 40}{space 2} .3594831{col 51}{space 1}   -0.09{col 60}{space 3}0.927{col 68}{space 4} .4662379{col 81}{space 3} 2.003555
{txt}{space 23}19  {c |}{col 28}{res}{space 2} 1.870537{col 40}{space 2} .5773721{col 51}{space 1}    2.03{col 60}{space 3}0.042{col 68}{space 4} 1.021478{col 81}{space 3}  3.42534
{txt}{space 23}20  {c |}{col 28}{res}{space 2}  .452801{col 40}{space 2} .2631816{col 51}{space 1}   -1.36{col 60}{space 3}0.173{col 68}{space 4} .1449317{col 81}{space 3} 1.414658
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .2710928{col 40}{space 2} .0900681{col 51}{space 1}   -3.93{col 60}{space 3}0.000{col 68}{space 4} .1413559{col 81}{space 3} .5199027
{txt}{space 23}22  {c |}{col 28}{res}{space 2} 1.373508{col 40}{space 2} .7224843{col 51}{space 1}    0.60{col 60}{space 3}0.546{col 68}{space 4}  .489877{col 81}{space 3} 3.851013
{txt}{space 23}23  {c |}{col 28}{res}{space 2} 2.099938{col 40}{space 2} 1.055242{col 51}{space 1}    1.48{col 60}{space 3}0.140{col 68}{space 4} .7842747{col 81}{space 3} 5.622696
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .0242796{col 40}{space 2} .0317717{col 51}{space 1}   -2.84{col 60}{space 3}0.004{col 68}{space 4}  .001868{col 81}{space 3} .3155758
{txt}{space 23}25  {c |}{col 28}{res}{space 2} .8072485{col 40}{space 2} .2376273{col 51}{space 1}   -0.73{col 60}{space 3}0.467{col 68}{space 4} .4533584{col 81}{space 3} 1.437384
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .9453992{col 40}{space 2} .3591647{col 51}{space 1}   -0.15{col 60}{space 3}0.883{col 68}{space 4} .4489903{col 81}{space 3} 1.990643
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 2.851923{col 40}{space 2}  1.16075{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4}  1.28438{col 81}{space 3} 6.332598
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 1.069588{col 40}{space 2} .4610951{col 51}{space 1}    0.16{col 60}{space 3}0.876{col 68}{space 4} .4594805{col 81}{space 3}  2.48981
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.292284{col 40}{space 2} .5719375{col 51}{space 1}    0.58{col 60}{space 3}0.562{col 68}{space 4} .5427927{col 81}{space 3} 3.076676
{txt}{space 23}50  {c |}{col 28}{res}{space 2} .7992572{col 40}{space 2} .2302847{col 51}{space 1}   -0.78{col 60}{space 3}0.437{col 68}{space 4}  .454397{col 81}{space 3} 1.405846
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 2.841378{col 40}{space 2}  .932191{col 51}{space 1}    3.18{col 60}{space 3}0.001{col 68}{space 4}  1.49372{col 81}{space 3} 5.404913
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.584075{col 40}{space 2} 1.377793{col 51}{space 1}    0.53{col 60}{space 3}0.597{col 68}{space 4} .2880181{col 81}{space 3} 8.712281
{txt}{space 23}53  {c |}{col 28}{res}{space 2}  1.23831{col 40}{space 2} .1048784{col 51}{space 1}    2.52{col 60}{space 3}0.012{col 68}{space 4} 1.048907{col 81}{space 3} 1.461913
{txt}{space 23}54  {c |}{col 28}{res}{space 2} .3372951{col 40}{space 2} .3321381{col 51}{space 1}   -1.10{col 60}{space 3}0.270{col 68}{space 4} .0489579{col 81}{space 3} 2.323792
{txt}{space 23}56  {c |}{col 28}{res}{space 2} .4599093{col 40}{space 2}  .197993{col 51}{space 1}   -1.80{col 60}{space 3}0.071{col 68}{space 4} .1977999{col 81}{space 3} 1.069346
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 2.175009{col 40}{space 2} 2.044455{col 51}{space 1}    0.83{col 60}{space 3}0.408{col 68}{space 4} .3446297{col 81}{space 3}  13.7268
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .1099954{col 40}{space 2} .1129135{col 51}{space 1}   -2.15{col 60}{space 3}0.032{col 68}{space 4} .0147093{col 81}{space 3} .8225409
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .4281996{col 40}{space 2} .1522779{col 51}{space 1}   -2.39{col 60}{space 3}0.017{col 68}{space 4} .2132745{col 81}{space 3} .8597131
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 9.22e+67{col 40}{space 2} 7.09e+69{col 51}{space 1}    2.04{col 60}{space 3}0.042{col 68}{space 4} 353.2126{col 81}{space 3} 2.4e+133
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} 1.431208{col 40}{space 2}  .087695{col 51}{space 1}   16.32{col 60}{space 3}0.000{col 68}{space 4} 1.259329{col 81}{space 3} 1.603087
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 4.183751{col 40}{space 2} .3668942{col 68}{space 4} 3.523057{col 81}{space 3} 4.968348
{txt}                       1/p {c |}{col 28}{res}{space 2}   .23902{col 40}{space 2} .0209609{col 68}{space 4} .2012742{col 81}{space 3} .2838444
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelF4Aa
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.4385928  0.401878))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4385928}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}.401878}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 875.5949{col 26}{space 2} 34.50461{col 37}{space 1}   25.38{col 46}{space 3}0.000{col 54}{space 4} 807.9671{col 67}{space 3} 943.2227
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 951.2022{col 26}{space 2} 19.21363{col 37}{space 1}   49.51{col 46}{space 3}0.000{col 54}{space 4} 913.5442{col 67}{space 3} 988.8602
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.4385928  0.401878))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.4385928}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 4}.401878}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     4.08{col 38}{space 2}   0.0434
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  75.6073{col 26}{space 2} 37.43487{col 37}{space 5} 2.236305{col 51}{space 3} 148.9783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF4Aazloyal = r(table)
{txt}
{com}. mat list modelF4Aazloyal
{res}
{txt}modelF4Aazloyal[9,1]
            r2vs1.
              _at
     b {res} 75.607298
{txt}    se {res} 37.434868
{txt}     z {res} 2.0197025
{txt}pvalue {res} .04341425
{txt}    ll {res} 2.2363053
{txt}    ul {res} 148.97829
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modelF4Aa
{txt}(results {stata estimates replay modelF4Aa:modelF4Aa} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.8003148 1.753017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.8003148}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.753017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 844.9337{col 26}{space 2} 47.31455{col 37}{space 1}   17.86{col 46}{space 3}0.000{col 54}{space 4} 752.1988{col 67}{space 3} 937.6685
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  1086.67{col 26}{space 2} 84.10678{col 37}{space 1}   12.92{col 46}{space 3}0.000{col 54}{space 4} 921.8234{col 67}{space 3} 1251.516
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.8003148 1.753017))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       246
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.8003148}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.753017}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     3.67{col 38}{space 2}   0.0555
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  241.736{col 26}{space 2} 126.2491{col 37}{space 5}-5.707802{col 51}{space 3} 489.1797
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF4Abzloyal = r(table)
{txt}
{com}. mat list modelF4Abzloyal
{res}
{txt}modelF4Abzloyal[9,1]
             r2vs1.
               _at
     b {res}  241.73597
{txt}    se {res}  126.24914
{txt}     z {res}  1.9147534
{txt}pvalue {res}  .05552396
{txt}    ll {res} -5.7078022
{txt}    ul {res}  489.17974
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          0
{reset}
{com}. 
. 
. 
. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** MODEL F4B: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency clinton bush43 if reagan==1 | clinton==1 | bush43==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-726.47593
{txt}Iteration 1:   log pseudolikelihood = {res}-593.65309
{txt}Iteration 2:   log pseudolikelihood = {res}-589.88411
{txt}Iteration 3:   log pseudolikelihood = {res}-589.88292
{txt}Iteration 4:   log pseudolikelihood = {res}-589.88292

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-589.88292}  
Iteration 1:{space 3}log pseudolikelihood = {res: -474.2245}  
Iteration 2:{space 3}log pseudolikelihood = {res:-372.58416}  
Iteration 3:{space 3}log pseudolikelihood = {res:-369.95307}  
Iteration 4:{space 3}log pseudolikelihood = {res:-369.93954}  
Iteration 5:{space 3}log pseudolikelihood = {res:-369.93954}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
{col 49}{help j_robustsingular##|_new:Wald chi2(20)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-369.93954             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2}  1.37595{col 48}{space 2} .1793624{col 59}{space 1}    2.45{col 68}{space 3}0.014{col 76}{space 4} 1.065722{col 89}{space 3} 1.776483
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.212228{col 48}{space 2} .2523521{col 59}{space 1}    0.92{col 68}{space 3}0.355{col 76}{space 4} .8060982{col 89}{space 3} 1.822976
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2}  .598382{col 48}{space 2} .0945698{col 59}{space 1}   -3.25{col 68}{space 3}0.001{col 76}{space 4} .4389877{col 89}{space 3} .8156514
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} .9945713{col 48}{space 2}  .083893{col 59}{space 1}   -0.06{col 68}{space 3}0.949{col 76}{space 4} .8430169{col 89}{space 3} 1.173372
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} 1.030779{col 48}{space 2} .0687108{col 59}{space 1}    0.45{col 68}{space 3}0.649{col 76}{space 4} .9045344{col 89}{space 3} 1.174643
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2}  .499528{col 48}{space 2} .0572595{col 59}{space 1}   -6.06{col 68}{space 3}0.000{col 76}{space 4} .3990148{col 89}{space 3} .6253609
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2}  .730317{col 48}{space 2} .2526573{col 59}{space 1}   -0.91{col 68}{space 3}0.364{col 76}{space 4} .3707092{col 89}{space 3} 1.438764
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .7215764{col 48}{space 2}  .250208{col 59}{space 1}   -0.94{col 68}{space 3}0.347{col 76}{space 4} .3657013{col 89}{space 3} 1.423764
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.527561{col 48}{space 2} .3929044{col 59}{space 1}    1.65{col 68}{space 3}0.100{col 76}{space 4} .9227009{col 89}{space 3} 2.528928
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.429509{col 48}{space 2} .4898696{col 59}{space 1}    1.04{col 68}{space 3}0.297{col 76}{space 4} .7302875{col 89}{space 3} 2.798207
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 1.63e-07{col 48}{space 2} 1.56e-06{col 59}{space 1}   -1.63{col 68}{space 3}0.103{col 76}{space 4} 1.11e-15{col 89}{space 3} 23.90106
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 55.04939{col 48}{space 2} 114.6379{col 59}{space 1}    1.92{col 68}{space 3}0.054{col 76}{space 4} .9293173{col 89}{space 3} 3260.927
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2315691{col 48}{space 2} .0410594{col 59}{space 1}   -8.25{col 68}{space 3}0.000{col 76}{space 4}  .163589{col 89}{space 3} .3277984
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9996725{col 48}{space 2} .0057518{col 59}{space 1}   -0.06{col 68}{space 3}0.955{col 76}{space 4} .9884625{col 89}{space 3}  1.01101
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.032696{col 48}{space 2} .1045948{col 59}{space 1}    0.32{col 68}{space 3}0.751{col 76}{space 4} .8467598{col 89}{space 3} 1.259462
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.485732{col 48}{space 2} .4796457{col 59}{space 1}    1.23{col 68}{space 3}0.220{col 76}{space 4} .7891204{col 89}{space 3} 2.797292
{txt}{space 32}3  {c |}{col 36}{res}{space 2}  3.49034{col 48}{space 2} 1.128816{col 59}{space 1}    3.87{col 68}{space 3}0.000{col 76}{space 4} 1.851738{col 89}{space 3}  6.57894
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.934501{col 48}{space 2}  .644701{col 59}{space 1}    1.98{col 68}{space 3}0.048{col 76}{space 4} 1.006685{col 89}{space 3} 3.717444
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.254303{col 48}{space 2}  .317462{col 59}{space 1}    0.90{col 68}{space 3}0.371{col 76}{space 4} .7637736{col 89}{space 3} 2.059872
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.655232{col 48}{space 2} .6419937{col 59}{space 1}    4.04{col 68}{space 3}0.000{col 76}{space 4} 1.653085{col 89}{space 3} 4.264909
{txt}{space 32}7  {c |}{col 36}{res}{space 2}  5.97326{col 48}{space 2} 1.778191{col 59}{space 1}    6.00{col 68}{space 3}0.000{col 76}{space 4} 3.332849{col 89}{space 3} 10.70551
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 8.448879{col 48}{space 2} 3.593233{col 59}{space 1}    5.02{col 68}{space 3}0.000{col 76}{space 4} 3.671053{col 89}{space 3} 19.44498
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.229887{col 48}{space 2} .7837354{col 59}{space 1}    2.28{col 68}{space 3}0.023{col 76}{space 4} 1.119727{col 89}{space 3} 4.440721
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.109441{col 48}{space 2} .7290209{col 59}{space 1}    2.16{col 68}{space 3}0.031{col 76}{space 4} 1.071502{col 89}{space 3} 4.152808
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.444357{col 48}{space 2} .3531819{col 59}{space 1}    1.50{col 68}{space 3}0.133{col 76}{space 4} .8944051{col 89}{space 3} 2.332464
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.082341{col 48}{space 2} .3158813{col 59}{space 1}    0.27{col 68}{space 3}0.786{col 76}{space 4} .6108591{col 89}{space 3}  1.91773
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.925858{col 48}{space 2} .6765164{col 59}{space 1}    4.64{col 68}{space 3}0.000{col 76}{space 4} 1.859682{col 89}{space 3} 4.603286
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.496428{col 48}{space 2} .5580168{col 59}{space 1}    1.08{col 68}{space 3}0.280{col 76}{space 4} .7205157{col 89}{space 3} 3.107908
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.086154{col 48}{space 2} .6935796{col 59}{space 1}    2.21{col 68}{space 3}0.027{col 76}{space 4} 1.087299{col 89}{space 3} 4.002614
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 1.935442{col 48}{space 2} .5646847{col 59}{space 1}    2.26{col 68}{space 3}0.024{col 76}{space 4} 1.092531{col 89}{space 3} 3.428679
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 3.660165{col 48}{space 2} 1.424033{col 59}{space 1}    3.33{col 68}{space 3}0.001{col 76}{space 4} 1.707379{col 89}{space 3} 7.846416
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.842706{col 48}{space 2} .5091776{col 59}{space 1}    2.21{col 68}{space 3}0.027{col 76}{space 4}  1.07214{col 89}{space 3} 3.167091
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.834615{col 48}{space 2} .5812093{col 59}{space 1}    1.92{col 68}{space 3}0.055{col 76}{space 4} .9860135{col 89}{space 3} 3.413557
{txt}{space 31}14  {c |}{col 36}{res}{space 2}  2.05867{col 48}{space 2} .7064726{col 59}{space 1}    2.10{col 68}{space 3}0.035{col 76}{space 4} 1.050704{col 89}{space 3} 4.033602
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.576771{col 48}{space 2} .5551972{col 59}{space 1}    1.29{col 68}{space 3}0.196{col 76}{space 4} .7907726{col 89}{space 3} 3.144021
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .7527716{col 48}{space 2} .1628058{col 59}{space 1}   -1.31{col 68}{space 3}0.189{col 76}{space 4} .4926852{col 89}{space 3} 1.150157
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.244473{col 48}{space 2} .1165169{col 59}{space 1}    2.34{col 68}{space 3}0.019{col 76}{space 4} 1.035832{col 89}{space 3} 1.495138
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.031796{col 48}{space 2} .7553509{col 59}{space 1}    1.91{col 68}{space 3}0.057{col 76}{space 4} .9804672{col 89}{space 3} 4.210437
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .8347512{col 48}{space 2} .1384023{col 59}{space 1}   -1.09{col 68}{space 3}0.276{col 76}{space 4} .6031527{col 89}{space 3} 1.155279
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .3147406{col 48}{space 2} .1090684{col 59}{space 1}   -3.34{col 68}{space 3}0.001{col 76}{space 4} .1595813{col 89}{space 3} .6207594
{txt}{space 31}21  {c |}{col 36}{res}{space 2} 1.404684{col 48}{space 2} .2241406{col 59}{space 1}    2.13{col 68}{space 3}0.033{col 76}{space 4} 1.027437{col 89}{space 3} 1.920445
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .5058724{col 48}{space 2} .2079541{col 59}{space 1}   -1.66{col 68}{space 3}0.097{col 76}{space 4} .2260106{col 89}{space 3} 1.132278
{txt}{space 31}23  {c |}{col 36}{res}{space 2}  1.44807{col 48}{space 2} .4954117{col 59}{space 1}    1.08{col 68}{space 3}0.279{col 76}{space 4} .7405897{col 89}{space 3} 2.831401
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .5907092{col 48}{space 2} .2149786{col 59}{space 1}   -1.45{col 68}{space 3}0.148{col 76}{space 4} .2894632{col 89}{space 3} 1.205464
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.920198{col 48}{space 2} .2979752{col 59}{space 1}    4.20{col 68}{space 3}0.000{col 76}{space 4} 1.416632{col 89}{space 3} 2.602766
{txt}{space 31}26  {c |}{col 36}{res}{space 2}  .630674{col 48}{space 2}  .118316{col 59}{space 1}   -2.46{col 68}{space 3}0.014{col 76}{space 4} .4366339{col 89}{space 3} .9109455
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} .8822352{col 48}{space 2} .1448563{col 59}{space 1}   -0.76{col 68}{space 3}0.445{col 76}{space 4} .6394749{col 89}{space 3} 1.217153
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 5.010118{col 48}{space 2} 1.934445{col 59}{space 1}    4.17{col 68}{space 3}0.000{col 76}{space 4} 2.350674{col 89}{space 3} 10.67833
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.523467{col 48}{space 2} .4587919{col 59}{space 1}    1.40{col 68}{space 3}0.162{col 76}{space 4} .8442942{col 89}{space 3} 2.748984
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.127195{col 48}{space 2} .4764367{col 59}{space 1}    3.37{col 68}{space 3}0.001{col 76}{space 4} 1.371387{col 89}{space 3} 3.299548
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.605948{col 48}{space 2} .6940607{col 59}{space 1}    3.60{col 68}{space 3}0.000{col 76}{space 4} 1.546177{col 89}{space 3} 4.392101
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.712803{col 48}{space 2}  .636739{col 59}{space 1}    1.45{col 68}{space 3}0.148{col 76}{space 4} .8265533{col 89}{space 3} 3.549309
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.518404{col 48}{space 2} .3624835{col 59}{space 1}    1.75{col 68}{space 3}0.080{col 76}{space 4} .9510047{col 89}{space 3}  2.42433
{txt}{space 31}54  {c |}{col 36}{res}{space 2}  2.22521{col 48}{space 2} .4680483{col 59}{space 1}    3.80{col 68}{space 3}0.000{col 76}{space 4} 1.473432{col 89}{space 3} 3.360561
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.166298{col 48}{space 2} .4368993{col 59}{space 1}    0.41{col 68}{space 3}0.681{col 76}{space 4} .5596884{col 89}{space 3} 2.430371
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .7838663{col 48}{space 2} .3474954{col 59}{space 1}   -0.55{col 68}{space 3}0.583{col 76}{space 4}  .328773{col 89}{space 3} 1.868907
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.093555{col 48}{space 2} .3749785{col 59}{space 1}    0.26{col 68}{space 3}0.794{col 76}{space 4} .5584251{col 89}{space 3} 2.141492
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .4754329{col 48}{space 2} .1159677{col 59}{space 1}   -3.05{col 68}{space 3}0.002{col 76}{space 4} .2947569{col 89}{space 3} .7668573
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .7788557{col 48}{space 2} .1162817{col 59}{space 1}   -1.67{col 68}{space 3}0.094{col 76}{space 4} .5812651{col 89}{space 3} 1.043614
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 27}clinton {c |}{col 36}{res}{space 2} 3.183787{col 48}{space 2} 2.832581{col 59}{space 1}    1.30{col 68}{space 3}0.193{col 76}{space 4} .5567231{col 89}{space 3} 18.20744
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} 2.037506{col 48}{space 2} 1.297821{col 59}{space 1}    1.12{col 68}{space 3}0.264{col 76}{space 4} .5846732{col 89}{space 3} 7.100431
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 3.92e-06{col 48}{space 2} .0000191{col 59}{space 1}   -2.55{col 68}{space 3}0.011{col 76}{space 4} 2.79e-10{col 89}{space 3}  .055137
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9689254{col 48}{space 2} .0324471{col 59}{space 1}   29.86{col 68}{space 3}0.000{col 76}{space 4} .9053303{col 89}{space 3} 1.032521
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.635111{col 48}{space 2} .0855016{col 76}{space 4} 2.472749{col 89}{space 3} 2.808135
{txt}                               1/p {c |}{col 36}{res}{space 2} .3794906{col 48}{space 2} .0123134{col 76}{space 4} .3561083{col 89}{space 3} .4044083
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       614{col 28}-589.8829{col 39}-369.9395{col 50}    22{col 58} 783.8791{col 69}  881.119
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. estimates store modelF4B
{txt}
{com}. estout modelF4B, cells(b(star fmt(3)) se(par fmt(3))) eform
{res}
{txt}{hline 28}
{txt}                 modelF4B   
{txt}                     b/se   
{txt}{hline 28}
{res}_t                          {txt}
{txt}zloyalmedian{res}        1.376*  {txt}
            {res}      (0.179)   {txt}
{txt}0.soubinar~m{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~m{res}        1.212   {txt}
            {res}      (0.252)   {txt}
{txt}0.soubinar~i{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}1.soubinar~i{res}        0.598** {txt}
            {res}      (0.095)   {txt}
{txt}zpecompmed~n{res}        0.995   {txt}
            {res}      (0.084)   {txt}
{txt}zmecompmed~n{res}        1.031   {txt}
            {res}      (0.069)   {txt}
{txt}toplevel2   {res}        0.500***{txt}
            {res}      (0.057)   {txt}
{txt}presagency~n{res}        0.730   {txt}
            {res}      (0.253)   {txt}
{txt}presagency~d{res}        0.722   {txt}
            {res}      (0.250)   {txt}
{txt}subagencyd~n{res}        1.528   {txt}
            {res}      (0.393)   {txt}
{txt}standalone~n{res}        1.430   {txt}
            {res}      (0.490)   {txt}
{txt}okstartsen~n{res}        0.000   {txt}
            {res}      (0.000)   {txt}
{txt}okstartfil~e{res}       55.049   {txt}
            {res}    (114.638)   {txt}
{txt}okcrossover {res}        0.232***{txt}
            {res}      (0.041)   {txt}
{txt}okstartpre~p{res}        1.000   {txt}
            {res}      (0.006)   {txt}
{txt}okstartune~t{res}        1.033   {txt}
            {res}      (0.105)   {txt}
{txt}1.okstarta~r{res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.okstarta~r{res}        1.486   {txt}
            {res}      (0.480)   {txt}
{txt}3.okstarta~r{res}        3.490***{txt}
            {res}      (1.129)   {txt}
{txt}4.okstarta~r{res}        1.935*  {txt}
            {res}      (0.645)   {txt}
{txt}5.okstarta~r{res}        1.254   {txt}
            {res}      (0.317)   {txt}
{txt}6.okstarta~r{res}        2.655***{txt}
            {res}      (0.642)   {txt}
{txt}7.okstarta~r{res}        5.973***{txt}
            {res}      (1.778)   {txt}
{txt}8.okstarta~r{res}        8.449***{txt}
            {res}      (3.593)   {txt}
{txt}1.sbagency  {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}2.sbagency  {res}        2.230*  {txt}
            {res}      (0.784)   {txt}
{txt}3.sbagency  {res}        2.109*  {txt}
            {res}      (0.729)   {txt}
{txt}4.sbagency  {res}        1.444   {txt}
            {res}      (0.353)   {txt}
{txt}5.sbagency  {res}        1.082   {txt}
            {res}      (0.316)   {txt}
{txt}6.sbagency  {res}        2.926***{txt}
            {res}      (0.677)   {txt}
{txt}7.sbagency  {res}        1.496   {txt}
            {res}      (0.558)   {txt}
{txt}8.sbagency  {res}        2.086*  {txt}
            {res}      (0.694)   {txt}
{txt}9.sbagency  {res}        1.935*  {txt}
            {res}      (0.565)   {txt}
{txt}11.sbagency {res}        3.660***{txt}
            {res}      (1.424)   {txt}
{txt}12.sbagency {res}        1.843*  {txt}
            {res}      (0.509)   {txt}
{txt}13.sbagency {res}        1.835   {txt}
            {res}      (0.581)   {txt}
{txt}14.sbagency {res}        2.059*  {txt}
            {res}      (0.706)   {txt}
{txt}15.sbagency {res}        1.577   {txt}
            {res}      (0.555)   {txt}
{txt}16.sbagency {res}        0.753   {txt}
            {res}      (0.163)   {txt}
{txt}17.sbagency {res}        1.244*  {txt}
            {res}      (0.117)   {txt}
{txt}18.sbagency {res}        2.032   {txt}
            {res}      (0.755)   {txt}
{txt}19.sbagency {res}        0.835   {txt}
            {res}      (0.138)   {txt}
{txt}20.sbagency {res}        0.315***{txt}
            {res}      (0.109)   {txt}
{txt}21.sbagency {res}        1.405*  {txt}
            {res}      (0.224)   {txt}
{txt}22.sbagency {res}        0.506   {txt}
            {res}      (0.208)   {txt}
{txt}23.sbagency {res}        1.448   {txt}
            {res}      (0.495)   {txt}
{txt}24.sbagency {res}        0.591   {txt}
            {res}      (0.215)   {txt}
{txt}25.sbagency {res}        1.920***{txt}
            {res}      (0.298)   {txt}
{txt}26.sbagency {res}        0.631*  {txt}
            {res}      (0.118)   {txt}
{txt}27.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}28.sbagency {res}        0.882   {txt}
            {res}      (0.145)   {txt}
{txt}29.sbagency {res}        5.010***{txt}
            {res}      (1.934)   {txt}
{txt}30.sbagency {res}        1.523   {txt}
            {res}      (0.459)   {txt}
{txt}50.sbagency {res}        2.127***{txt}
            {res}      (0.476)   {txt}
{txt}51.sbagency {res}        2.606***{txt}
            {res}      (0.694)   {txt}
{txt}52.sbagency {res}        1.713   {txt}
            {res}      (0.637)   {txt}
{txt}53.sbagency {res}        1.518   {txt}
            {res}      (0.362)   {txt}
{txt}54.sbagency {res}        2.225***{txt}
            {res}      (0.468)   {txt}
{txt}55.sbagency {res}        1.166   {txt}
            {res}      (0.437)   {txt}
{txt}56.sbagency {res}        0.784   {txt}
            {res}      (0.347)   {txt}
{txt}57.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}58.sbagency {res}        1.094   {txt}
            {res}      (0.375)   {txt}
{txt}59.sbagency {res}        0.475** {txt}
            {res}      (0.116)   {txt}
{txt}60.sbagency {res}        0.779   {txt}
            {res}      (0.116)   {txt}
{txt}61.sbagency {res}        1.000   {txt}
            {res}          (.)   {txt}
{txt}clinton     {res}        3.184   {txt}
            {res}      (2.833)   {txt}
{txt}bush43      {res}        2.038   {txt}
            {res}      (1.298)   {txt}
{txt}_cons       {res}        0.000*  {txt}
            {res}      (0.000)   {txt}
{txt}{hline 28}
{res}/                           {txt}
{txt}ln_p        {res}        2.635***{txt}
            {res}      (0.086)   {txt}
{txt}{hline 28}

{com}. 
. 
. 
. *** COMPUTE Figure F1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQR = 1.3600037 [0.9816979 - (-0.3783058)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3600037, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3600037

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .4973818{col 26}{space 2} .1069065{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4} .3263878{col 67}{space 3} .7579594
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelF4Bzloyal = r(table)
{txt}
{com}. mat list modelF4Bzloyal
{res}
{txt}modelF4Bzloyal[9,1]
               (1)
     b {res}  .49738183
{txt}    se {res}  .10690652
{txt}     z {res} -3.2492885
{txt}pvalue {res}  .00115694
{txt}    ll {res}   .3263878
{txt}    ul {res}  .75795936
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure F2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [MF1B−MF4B] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3600037 [0.9816979 - (-0.3783058)]
. 
. ** Re-Estimate Model F4B  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency clinton bush43 if reagan==1 | clinton==1 | bush43==1, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-726.47593
{txt}Iteration 1:   log pseudolikelihood = {res}-593.65309
{txt}Iteration 2:   log pseudolikelihood = {res}-589.88411
{txt}Iteration 3:   log pseudolikelihood = {res}-589.88292
{txt}Iteration 4:   log pseudolikelihood = {res}-589.88292

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-589.88292}  
Iteration 1:{space 3}log pseudolikelihood = {res: -474.2245}  
Iteration 2:{space 3}log pseudolikelihood = {res:-372.58416}  
Iteration 3:{space 3}log pseudolikelihood = {res:-369.95307}  
Iteration 4:{space 3}log pseudolikelihood = {res:-369.93954}  
Iteration 5:{space 3}log pseudolikelihood = {res:-369.93954}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         614             {txt}Number of obs    =  {res}       614
{txt}No. of failures      = {res}         604
{txt}Time at risk         = {res}      644318
{col 49}{help j_robustsingular##|_new:Wald chi2(20)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-369.93954             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}  1.37595{col 40}{space 2} .1793624{col 51}{space 1}    2.45{col 60}{space 3}0.014{col 68}{space 4} 1.065722{col 81}{space 3} 1.776483
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.212228{col 40}{space 2} .2523521{col 51}{space 1}    0.92{col 60}{space 3}0.355{col 68}{space 4} .8060982{col 81}{space 3} 1.822976
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2}  .598382{col 40}{space 2} .0945698{col 51}{space 1}   -3.25{col 60}{space 3}0.001{col 68}{space 4} .4389877{col 81}{space 3} .8156514
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} .9945713{col 40}{space 2}  .083893{col 51}{space 1}   -0.06{col 60}{space 3}0.949{col 68}{space 4} .8430169{col 81}{space 3} 1.173372
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} 1.030779{col 40}{space 2} .0687108{col 51}{space 1}    0.45{col 60}{space 3}0.649{col 68}{space 4} .9045344{col 81}{space 3} 1.174643
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2}  .499528{col 40}{space 2} .0572595{col 51}{space 1}   -6.06{col 60}{space 3}0.000{col 68}{space 4} .3990148{col 81}{space 3} .6253609
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2}  .730317{col 40}{space 2} .2526573{col 51}{space 1}   -0.91{col 60}{space 3}0.364{col 68}{space 4} .3707092{col 81}{space 3} 1.438764
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .7215764{col 40}{space 2}  .250208{col 51}{space 1}   -0.94{col 60}{space 3}0.347{col 68}{space 4} .3657013{col 81}{space 3} 1.423764
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.527561{col 40}{space 2} .3929044{col 51}{space 1}    1.65{col 60}{space 3}0.100{col 68}{space 4} .9227009{col 81}{space 3} 2.528928
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.429509{col 40}{space 2} .4898696{col 51}{space 1}    1.04{col 60}{space 3}0.297{col 68}{space 4} .7302875{col 81}{space 3} 2.798207
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 1.63e-07{col 40}{space 2} 1.56e-06{col 51}{space 1}   -1.63{col 60}{space 3}0.103{col 68}{space 4} 1.11e-15{col 81}{space 3} 23.90106
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 55.04939{col 40}{space 2} 114.6379{col 51}{space 1}    1.92{col 60}{space 3}0.054{col 68}{space 4} .9293173{col 81}{space 3} 3260.927
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2315691{col 40}{space 2} .0410594{col 51}{space 1}   -8.25{col 60}{space 3}0.000{col 68}{space 4}  .163589{col 81}{space 3} .3277984
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9996725{col 40}{space 2} .0057518{col 51}{space 1}   -0.06{col 60}{space 3}0.955{col 68}{space 4} .9884625{col 81}{space 3}  1.01101
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.032696{col 40}{space 2} .1045948{col 51}{space 1}    0.32{col 60}{space 3}0.751{col 68}{space 4} .8467598{col 81}{space 3} 1.259462
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.485732{col 40}{space 2} .4796457{col 51}{space 1}    1.23{col 60}{space 3}0.220{col 68}{space 4} .7891204{col 81}{space 3} 2.797292
{txt}{space 24}3  {c |}{col 28}{res}{space 2}  3.49034{col 40}{space 2} 1.128816{col 51}{space 1}    3.87{col 60}{space 3}0.000{col 68}{space 4} 1.851738{col 81}{space 3}  6.57894
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.934501{col 40}{space 2}  .644701{col 51}{space 1}    1.98{col 60}{space 3}0.048{col 68}{space 4} 1.006685{col 81}{space 3} 3.717444
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.254303{col 40}{space 2}  .317462{col 51}{space 1}    0.90{col 60}{space 3}0.371{col 68}{space 4} .7637736{col 81}{space 3} 2.059872
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.655232{col 40}{space 2} .6419937{col 51}{space 1}    4.04{col 60}{space 3}0.000{col 68}{space 4} 1.653085{col 81}{space 3} 4.264909
{txt}{space 24}7  {c |}{col 28}{res}{space 2}  5.97326{col 40}{space 2} 1.778191{col 51}{space 1}    6.00{col 60}{space 3}0.000{col 68}{space 4} 3.332849{col 81}{space 3} 10.70551
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 8.448879{col 40}{space 2} 3.593233{col 51}{space 1}    5.02{col 60}{space 3}0.000{col 68}{space 4} 3.671053{col 81}{space 3} 19.44498
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.229887{col 40}{space 2} .7837354{col 51}{space 1}    2.28{col 60}{space 3}0.023{col 68}{space 4} 1.119727{col 81}{space 3} 4.440721
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 2.109441{col 40}{space 2} .7290209{col 51}{space 1}    2.16{col 60}{space 3}0.031{col 68}{space 4} 1.071502{col 81}{space 3} 4.152808
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.444357{col 40}{space 2} .3531819{col 51}{space 1}    1.50{col 60}{space 3}0.133{col 68}{space 4} .8944051{col 81}{space 3} 2.332464
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.082341{col 40}{space 2} .3158813{col 51}{space 1}    0.27{col 60}{space 3}0.786{col 68}{space 4} .6108591{col 81}{space 3}  1.91773
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.925858{col 40}{space 2} .6765164{col 51}{space 1}    4.64{col 60}{space 3}0.000{col 68}{space 4} 1.859682{col 81}{space 3} 4.603286
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.496428{col 40}{space 2} .5580168{col 51}{space 1}    1.08{col 60}{space 3}0.280{col 68}{space 4} .7205157{col 81}{space 3} 3.107908
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.086154{col 40}{space 2} .6935796{col 51}{space 1}    2.21{col 60}{space 3}0.027{col 68}{space 4} 1.087299{col 81}{space 3} 4.002614
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 1.935442{col 40}{space 2} .5646847{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4} 1.092531{col 81}{space 3} 3.428679
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.660165{col 40}{space 2} 1.424033{col 51}{space 1}    3.33{col 60}{space 3}0.001{col 68}{space 4} 1.707379{col 81}{space 3} 7.846416
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.842706{col 40}{space 2} .5091776{col 51}{space 1}    2.21{col 60}{space 3}0.027{col 68}{space 4}  1.07214{col 81}{space 3} 3.167091
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.834615{col 40}{space 2} .5812093{col 51}{space 1}    1.92{col 60}{space 3}0.055{col 68}{space 4} .9860135{col 81}{space 3} 3.413557
{txt}{space 23}14  {c |}{col 28}{res}{space 2}  2.05867{col 40}{space 2} .7064726{col 51}{space 1}    2.10{col 60}{space 3}0.035{col 68}{space 4} 1.050704{col 81}{space 3} 4.033602
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.576771{col 40}{space 2} .5551972{col 51}{space 1}    1.29{col 60}{space 3}0.196{col 68}{space 4} .7907726{col 81}{space 3} 3.144021
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .7527716{col 40}{space 2} .1628058{col 51}{space 1}   -1.31{col 60}{space 3}0.189{col 68}{space 4} .4926852{col 81}{space 3} 1.150157
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.244473{col 40}{space 2} .1165169{col 51}{space 1}    2.34{col 60}{space 3}0.019{col 68}{space 4} 1.035832{col 81}{space 3} 1.495138
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 2.031796{col 40}{space 2} .7553509{col 51}{space 1}    1.91{col 60}{space 3}0.057{col 68}{space 4} .9804672{col 81}{space 3} 4.210437
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .8347512{col 40}{space 2} .1384023{col 51}{space 1}   -1.09{col 60}{space 3}0.276{col 68}{space 4} .6031527{col 81}{space 3} 1.155279
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3147406{col 40}{space 2} .1090684{col 51}{space 1}   -3.34{col 60}{space 3}0.001{col 68}{space 4} .1595813{col 81}{space 3} .6207594
{txt}{space 23}21  {c |}{col 28}{res}{space 2} 1.404684{col 40}{space 2} .2241406{col 51}{space 1}    2.13{col 60}{space 3}0.033{col 68}{space 4} 1.027437{col 81}{space 3} 1.920445
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .5058724{col 40}{space 2} .2079541{col 51}{space 1}   -1.66{col 60}{space 3}0.097{col 68}{space 4} .2260106{col 81}{space 3} 1.132278
{txt}{space 23}23  {c |}{col 28}{res}{space 2}  1.44807{col 40}{space 2} .4954117{col 51}{space 1}    1.08{col 60}{space 3}0.279{col 68}{space 4} .7405897{col 81}{space 3} 2.831401
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .5907092{col 40}{space 2} .2149786{col 51}{space 1}   -1.45{col 60}{space 3}0.148{col 68}{space 4} .2894632{col 81}{space 3} 1.205464
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.920198{col 40}{space 2} .2979752{col 51}{space 1}    4.20{col 60}{space 3}0.000{col 68}{space 4} 1.416632{col 81}{space 3} 2.602766
{txt}{space 23}26  {c |}{col 28}{res}{space 2}  .630674{col 40}{space 2}  .118316{col 51}{space 1}   -2.46{col 60}{space 3}0.014{col 68}{space 4} .4366339{col 81}{space 3} .9109455
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} .8822352{col 40}{space 2} .1448563{col 51}{space 1}   -0.76{col 60}{space 3}0.445{col 68}{space 4} .6394749{col 81}{space 3} 1.217153
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 5.010118{col 40}{space 2} 1.934445{col 51}{space 1}    4.17{col 60}{space 3}0.000{col 68}{space 4} 2.350674{col 81}{space 3} 10.67833
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.523467{col 40}{space 2} .4587919{col 51}{space 1}    1.40{col 60}{space 3}0.162{col 68}{space 4} .8442942{col 81}{space 3} 2.748984
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 2.127195{col 40}{space 2} .4764367{col 51}{space 1}    3.37{col 60}{space 3}0.001{col 68}{space 4} 1.371387{col 81}{space 3} 3.299548
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 2.605948{col 40}{space 2} .6940607{col 51}{space 1}    3.60{col 60}{space 3}0.000{col 68}{space 4} 1.546177{col 81}{space 3} 4.392101
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.712803{col 40}{space 2}  .636739{col 51}{space 1}    1.45{col 60}{space 3}0.148{col 68}{space 4} .8265533{col 81}{space 3} 3.549309
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.518404{col 40}{space 2} .3624835{col 51}{space 1}    1.75{col 60}{space 3}0.080{col 68}{space 4} .9510047{col 81}{space 3}  2.42433
{txt}{space 23}54  {c |}{col 28}{res}{space 2}  2.22521{col 40}{space 2} .4680483{col 51}{space 1}    3.80{col 60}{space 3}0.000{col 68}{space 4} 1.473432{col 81}{space 3} 3.360561
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.166298{col 40}{space 2} .4368993{col 51}{space 1}    0.41{col 60}{space 3}0.681{col 68}{space 4} .5596884{col 81}{space 3} 2.430371
{txt}{space 23}56  {c |}{col 28}{res}{space 2} .7838663{col 40}{space 2} .3474954{col 51}{space 1}   -0.55{col 60}{space 3}0.583{col 68}{space 4}  .328773{col 81}{space 3} 1.868907
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 1.093555{col 40}{space 2} .3749785{col 51}{space 1}    0.26{col 60}{space 3}0.794{col 68}{space 4} .5584251{col 81}{space 3} 2.141492
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .4754329{col 40}{space 2} .1159677{col 51}{space 1}   -3.05{col 60}{space 3}0.002{col 68}{space 4} .2947569{col 81}{space 3} .7668573
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .7788557{col 40}{space 2} .1162817{col 51}{space 1}   -1.67{col 60}{space 3}0.094{col 68}{space 4} .5812651{col 81}{space 3} 1.043614
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 19}clinton {c |}{col 28}{res}{space 2} 3.183787{col 40}{space 2} 2.832581{col 51}{space 1}    1.30{col 60}{space 3}0.193{col 68}{space 4} .5567231{col 81}{space 3} 18.20744
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} 2.037506{col 40}{space 2} 1.297821{col 51}{space 1}    1.12{col 60}{space 3}0.264{col 68}{space 4} .5846732{col 81}{space 3} 7.100431
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 3.92e-06{col 40}{space 2} .0000191{col 51}{space 1}   -2.55{col 60}{space 3}0.011{col 68}{space 4} 2.79e-10{col 81}{space 3}  .055137
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9689254{col 40}{space 2} .0324471{col 51}{space 1}   29.86{col 60}{space 3}0.000{col 68}{space 4} .9053303{col 81}{space 3} 1.032521
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.635111{col 40}{space 2} .0855016{col 68}{space 4} 2.472749{col 81}{space 3} 2.808135
{txt}                       1/p {c |}{col 28}{res}{space 2} .3794906{col 40}{space 2} .0123134{col 68}{space 4} .3561083{col 81}{space 3} .4044083
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modelF4Ba
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.3783058  0.9816979))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3783058}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9816979}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 951.4515{col 26}{space 2} 24.79783{col 37}{space 1}   38.37{col 46}{space 3}0.000{col 54}{space 4} 902.8486{col 67}{space 3} 1000.054
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1240.195{col 26}{space 2} 75.85647{col 37}{space 1}   16.35{col 46}{space 3}0.000{col 54}{space 4} 1091.519{col 67}{space 3} 1388.871
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.3783058  0.9816979))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3783058}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9816979}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.44{col 38}{space 2}   0.0021
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 288.7435{col 26}{space 2} 93.96278{col 37}{space 5} 104.5799{col 51}{space 3} 472.9072
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF4Bazloyal = r(table)
{txt}
{com}. mat list modelF4Bazloyal
{res}
{txt}modelF4Bazloyal[9,1]
            r2vs1.
              _at
     b {res} 288.74352
{txt}    se {res} 93.962778
{txt}     z {res} 3.0729564
{txt}pvalue {res} .00211949
{txt}    ll {res} 104.57986
{txt}    ul {res} 472.90718
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modelF4Ba
{txt}(results {stata estimates replay modelF4Ba:modelF4Ba} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6247732 1.690957))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6247732}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.690957}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 906.8323{col 26}{space 2} 35.31917{col 37}{space 1}   25.68{col 46}{space 3}0.000{col 54}{space 4}  837.608{col 67}{space 3} 976.0566
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1424.026{col 26}{space 2} 145.9169{col 37}{space 1}    9.76{col 46}{space 3}0.000{col 54}{space 4} 1138.034{col 67}{space 3} 1710.018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6247732 1.690957))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       614
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6247732}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.690957}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.53{col 38}{space 2}   0.0035
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 517.1933{col 26}{space 2} 177.1352{col 37}{space 5} 170.0148{col 51}{space 3} 864.3719
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelF4Bbzloyal = r(table)
{txt}
{com}. mat list modelF4Bbzloyal
{res}
{txt}modelF4Bbzloyal[9,1]
            r2vs1.
              _at
     b {res} 517.19334
{txt}    se {res} 177.13517
{txt}     z {res} 2.9197665
{txt}pvalue {res} .00350294
{txt}    ll {res} 170.01478
{txt}    ul {res} 864.37189
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *Figure F1
. 
. matrix pointmodel = modelF1Azloyal[1,1], modelF1Bzloyal[1,1], modelF2Azloyal[1,1], modelF2Bzloyal[1,1], modelF3Azloyal[1,1], modelF3Bzloyal[1,1], modelF4Azloyal[1,1], modelF4Bzloyal[1,1]
{txt}
{com}. 
. matrix cimodel = (modelF1Azloyal[5,1], modelF1Bzloyal[5,1], modelF2Azloyal[5,1], modelF2Bzloyal[5,1], modelF3Azloyal[5,1], modelF3Bzloyal[5,1], modelF4Azloyal[5,1], modelF4Bzloyal[5,1] \ modelF1Azloyal[6,1], modelF1Bzloyal[6,1], modelF2Azloyal[6,1], modelF2Bzloyal[6,1], modelF3Azloyal[6,1], modelF3Bzloyal[6,1], modelF4Azloyal[6,1], modelF4Bzloyal[6,1])
{txt}
{com}. 
. 
. 
. coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model F1A"  2 "Model F1B"  3 "Model F2A" 4 "Model F2B" 5 "Model F3A" 6 "Model F3B" 7 "Model F4A" 8 "Model F4B", labsize(small) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE F1", size(small)) ciopts(lcolor(black)) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small)) legend(order(17  18) lab(17 "FA Models Denote Single Term Presidents") lab(18 "FB Models Denote Two Term Presidents") rows(2) size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF1.gph saved)

{com}. 
. 
. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. *Figure F2
. 
. matrix pointmodelF1 = modelF3Aazloyal[1,1], modelF3Abzloyal[1,1], modelF3Bazloyal[1,1], modelF3Bbzloyal[1,1], modelF4Aazloyal[1,1], modelF4Abzloyal[1,1], modelF4Bazloyal[1,1], modelF4Bbzloyal[1,1]
{txt}
{com}. 
. matrix cimodel1 = (modelF3Aazloyal[5,1], modelF3Abzloyal[5,1], modelF3Bazloyal[5,1], modelF3Bbzloyal[5,1], modelF4Aazloyal[5,1], modelF4Abzloyal[5,1], modelF4Bazloyal[5,1], modelF4Bbzloyal[5,1] \ modelF3Aazloyal[6,1], modelF3Abzloyal[6,1], modelF3Bazloyal[6,1], modelF3Bbzloyal[6,1], modelF4Aazloyal[6,1], modelF4Abzloyal[6,1], modelF4Bazloyal[6,1], modelF4Bbzloyal[6,1])
{txt}
{com}. 
. *
. 
. coefplot (matrix(pointmodelF1), ci((cimodel1))), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model F3A" "Interquartile Change" "' 2 `" "Model F3A" "Interdecile Change" "' 3 `" "Model F3B" "Interquartile Change" "' 4 `" "Model F3B" "Interdecile Change" "' 5 `" "Model F4A" "Interquartile Change" "' 6 `" "Model F4A" "Interdecile Change" "' 7 `" "Model F4B" "Interquartile Change" "' 8 `" "Model F4B" "Interdecile Change" "', labsize(small) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(0(100)900, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE F2", size(small)) ciopts(lcolor(black)) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small)) legend(order(17  18) lab(17 "FA Models Denote Single Term Presidents") lab(18 "FB Models Denote Two Term Presidents") rows(2) size(small))
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureF2.gph saved)

{com}. 
. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX F.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 09:55:40
{txt}{.-}
{smcl}
{txt}{sf}{ul off}